{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/image-segmentation/papers/2","list_of":"/task/image-segmentation","task":"Image Segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":51,"rows_per_page":100,"rows":[101,200],"of":5035,"counts":{"archive_papers_tagged":5035,"with_a_code_link":2073,"where_syntology_ran_a_sample":378,"not_listed_spam_title":0,"listed":5035,"listed_where_code_ran":378,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":329,"every_run_a_failure_of_syntologys_instrument":49,"listed_with_a_run_with_no_instrument_failure":329,"listed_every_run_a_failure_of_syntologys_instrument":49,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/image-segmentation","prev":"/task/image-segmentation","next":"/task/image-segmentation/papers/3","papers":[{"url":"/paper/uctransnet-rethinking-the-skip-connections-in","slug":"uctransnet-rethinking-the-skip-connections-in","title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer","date":"2021-09-09","arxiv_id":"2109.04335","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/uctransnet-rethinking-the-skip-connections-in#ran","syntology_url":"https://syntology.ai/paper/2109.04335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04335"}},"official":{"repos":["mcgregorwwww/uctransnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/transbts-multimodal-brain-tumor-segmentation","slug":"transbts-multimodal-brain-tumor-segmentation","title":"TransBTS: Multimodal Brain Tumor Segmentation Using Transformer","date":"2021-03-07","arxiv_id":"2103.04430","repositories_listed":3,"syntology":null},{"url":"/paper/semi-supervised-left-atrium-segmentation-with","slug":"semi-supervised-left-atrium-segmentation-with","title":"Semi-supervised Left Atrium Segmentation with Mutual Consistency Training","date":"2021-03-04","arxiv_id":"2103.02911","repositories_listed":3,"syntology":null},{"url":"/paper/distribution-free-risk-controlling-prediction","slug":"distribution-free-risk-controlling-prediction","title":"Distribution-Free, Risk-Controlling Prediction Sets","date":"2021-01-07","arxiv_id":"2101.02703","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/distribution-free-risk-controlling-prediction#ran","syntology_url":"https://syntology.ai/paper/2101.02703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.02703"}},"official":{"repos":["aangelopoulos/rcps"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cutting-edge-3d-medical-image-segmentation","slug":"cutting-edge-3d-medical-image-segmentation","title":"Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?","date":"2021-01-01","arxiv_id":"2101.00232","repositories_listed":3,"syntology":null},{"url":"/paper/ca-net-comprehensive-attention-convolutional","slug":"ca-net-comprehensive-attention-convolutional","title":"CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation","date":"2020-09-22","arxiv_id":"2009.10549","repositories_listed":3,"syntology":null},{"url":"/paper/ma-net-a-multi-scale-attention-network-for","slug":"ma-net-a-multi-scale-attention-network-for","title":"MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation","date":"2020-09-21","arxiv_id":null,"repositories_listed":3,"syntology":null},{"url":"/paper/weakly-supervised-segmentation-with-multi","slug":"weakly-supervised-segmentation-with-multi","title":"Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates","date":"2020-07-02","arxiv_id":"2007.01152","repositories_listed":3,"syntology":null},{"url":"/paper/pyramidal-convolution-rethinking","slug":"pyramidal-convolution-rethinking","title":"Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition","date":"2020-06-20","arxiv_id":"2006.11538","repositories_listed":3,"syntology":null},{"url":"/paper/kiu-net-towards-accurate-segmentation-of","slug":"kiu-net-towards-accurate-segmentation-of","title":"KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations","date":"2020-06-08","arxiv_id":"2006.04878","repositories_listed":3,"syntology":null},{"url":"/paper/lung-segmentation-from-chest-x-rays-using","slug":"lung-segmentation-from-chest-x-rays-using","title":"Lung Segmentation from Chest X-rays using Variational Data Imputation","date":"2020-05-20","arxiv_id":"2005.10052","repositories_listed":3,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/lung-segmentation-from-chest-x-rays-using#ran","syntology_url":"https://syntology.ai/paper/2005.10052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10052"}},"official":{"repos":["raghavian/lungVAE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/saunet-shape-attentive-u-net-for","slug":"saunet-shape-attentive-u-net-for","title":"SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation","date":"2020-01-21","arxiv_id":"2001.07645","repositories_listed":3,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/saunet-shape-attentive-u-net-for#ran","syntology_url":"https://syntology.ai/paper/2001.07645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.07645"}},"official":{"repos":["sunjesse/shape-attentive-unet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/camel-a-weakly-supervised-learning-framework","slug":"camel-a-weakly-supervised-learning-framework","title":"CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation","date":"2019-08-28","arxiv_id":"1908.10555","repositories_listed":3,"syntology":null},{"url":"/paper/d-unet-a-dimension-fusion-u-shape-network-for","slug":"d-unet-a-dimension-fusion-u-shape-network-for","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","date":"2019-08-14","arxiv_id":"1908.05104","repositories_listed":3,"syntology":null},{"url":"/paper/deep-active-learning-for-axon-myelin","slug":"deep-active-learning-for-axon-myelin","title":"Deep Active Learning for Axon-Myelin Segmentation on Histology Data","date":"2019-07-11","arxiv_id":"1907.05143","repositories_listed":3,"syntology":null},{"url":"/paper/boundary-loss-for-remote-sensing-imagery","slug":"boundary-loss-for-remote-sensing-imagery","title":"Boundary Loss for Remote Sensing Imagery Semantic Segmentation","date":"2019-05-20","arxiv_id":"1905.07852","repositories_listed":3,"syntology":null},{"url":"/paper/bidirectional-learning-for-domain-adaptation","slug":"bidirectional-learning-for-domain-adaptation","title":"Bidirectional Learning for Domain Adaptation of Semantic Segmentation","date":"2019-04-24","arxiv_id":"1904.10620","repositories_listed":3,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bidirectional-learning-for-domain-adaptation#ran","syntology_url":"https://syntology.ai/paper/1904.10620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.10620"}},"official":{"repos":["liyunsheng13/BDL"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ce-net-context-encoder-network-for-2d-medical","slug":"ce-net-context-encoder-network-for-2d-medical","title":"CE-Net: Context Encoder Network for 2D Medical Image Segmentation","date":"2019-03-07","arxiv_id":"1903.02740","repositories_listed":3,"syntology":null},{"url":"/paper/clevr-ref-diagnosing-visual-reasoning-with","slug":"clevr-ref-diagnosing-visual-reasoning-with","title":"CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions","date":"2019-01-03","arxiv_id":"1901.00850","repositories_listed":3,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/clevr-ref-diagnosing-visual-reasoning-with#ran","syntology_url":"https://syntology.ai/paper/1901.00850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.00850"}},"official":null}},{"url":"/paper/unsupervised-meta-learning-of-figure-ground","slug":"unsupervised-meta-learning-of-figure-ground","title":"Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects","date":"2018-12-20","arxiv_id":"1812.08442","repositories_listed":3,"syntology":null},{"url":"/paper/global-deep-learning-methods-for","slug":"global-deep-learning-methods-for","title":"Non-local U-Net for Biomedical Image Segmentation","date":"2018-12-10","arxiv_id":"1812.04103","repositories_listed":3,"syntology":null},{"url":"/paper/laddernet-multi-path-networks-based-on-u-net","slug":"laddernet-multi-path-networks-based-on-u-net","title":"LadderNet: Multi-path networks based on U-Net for medical image segmentation","date":"2018-10-17","arxiv_id":"1810.07810","repositories_listed":3,"syntology":null},{"url":"/paper/hyperdense-net-a-hyper-densely-connected-cnn","slug":"hyperdense-net-a-hyper-densely-connected-cnn","title":"HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation","date":"2018-04-09","arxiv_id":"1804.02967","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hyperdense-net-a-hyper-densely-connected-cnn#ran","syntology_url":"https://syntology.ai/paper/1804.02967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.02967"}},"official":{"repos":["josedolz/HyperDenseNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/optimal-transport-for-multi-source-domain","slug":"optimal-transport-for-multi-source-domain","title":"Optimal Transport for Multi-source Domain Adaptation under Target Shift","date":"2018-03-13","arxiv_id":"1803.04899","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/optimal-transport-for-multi-source-domain#ran","syntology_url":"https://syntology.ai/paper/1803.04899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.04899"}},"official":{"repos":["PythonOT/POT"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/augmented-cyclegan-learning-many-to-many","slug":"augmented-cyclegan-learning-many-to-many","title":"Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data","date":"2018-02-27","arxiv_id":"1802.10151","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/augmented-cyclegan-learning-many-to-many#ran","syntology_url":"https://syntology.ai/paper/1802.10151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10151"}},"official":null}},{"url":"/paper/neural-networks-for-topology-optimization","slug":"neural-networks-for-topology-optimization","title":"Neural networks for topology optimization","date":"2017-09-27","arxiv_id":"1709.09578","repositories_listed":3,"syntology":null},{"url":"/paper/rotation-equivariant-vector-field-networks","slug":"rotation-equivariant-vector-field-networks","title":"Rotation equivariant vector field networks","date":"2016-12-29","arxiv_id":"1612.09346","repositories_listed":3,"syntology":null},{"url":"/paper/flood-filling-networks","slug":"flood-filling-networks","title":"Flood-Filling Networks","date":"2016-11-01","arxiv_id":"1611.00421","repositories_listed":3,"syntology":null},{"url":"/paper/voxresnet-deep-voxelwise-residual-networks","slug":"voxresnet-deep-voxelwise-residual-networks","title":"VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation","date":"2016-08-21","arxiv_id":"1608.05895","repositories_listed":3,"syntology":null},{"url":"/paper/exploring-models-and-data-for-image-question","slug":"exploring-models-and-data-for-image-question","title":"Exploring Models and Data for Image Question Answering","date":"2015-05-08","arxiv_id":"1505.02074","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-models-and-data-for-image-question#ran","syntology_url":"https://syntology.ai/paper/1505.02074","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1505.02074"}},"official":{"repos":["renmengye/imageqa-public"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/weakly-and-semi-supervised-learning-of-a-dcnn","slug":"weakly-and-semi-supervised-learning-of-a-dcnn","title":"Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation","date":"2015-02-09","arxiv_id":"1502.02734","repositories_listed":3,"syntology":null},{"url":"/paper/efficient-inference-in-fully-connected-crfs","slug":"efficient-inference-in-fully-connected-crfs","title":"Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials","date":"2012-10-20","arxiv_id":"1210.5644","repositories_listed":3,"syntology":null},{"url":"/paper/no-time-to-train-training-free-reference","slug":"no-time-to-train-training-free-reference","title":"No time to train! Training-Free Reference-Based Instance Segmentation","date":"2025-07-03","arxiv_id":"2507.02798","repositories_listed":2,"syntology":null},{"url":"/paper/svgdreamer-advancing-editability-and","slug":"svgdreamer-advancing-editability-and","title":"SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation","date":"2024-11-26","arxiv_id":"2411.17832","repositories_listed":2,"syntology":null},{"url":"/paper/fusionlungnet-multi-scale-fusion-convolution","slug":"fusionlungnet-multi-scale-fusion-convolution","title":"FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation","date":"2024-10-21","arxiv_id":"2410.15812","repositories_listed":2,"syntology":null},{"url":"/paper/medclip-samv2-towards-universal-text-driven","slug":"medclip-samv2-towards-universal-text-driven","title":"MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation","date":"2024-09-28","arxiv_id":"2409.19483","repositories_listed":2,"syntology":null},{"url":"/paper/assnet-adaptive-semantic-segmentation-network","slug":"assnet-adaptive-semantic-segmentation-network","title":"AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation","date":"2024-09-12","arxiv_id":"2409.07779","repositories_listed":2,"syntology":null},{"url":"/paper/sam2rad-a-segmentation-model-for-medical","slug":"sam2rad-a-segmentation-model-for-medical","title":"Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts","date":"2024-09-10","arxiv_id":"2409.06821","repositories_listed":2,"syntology":null},{"url":"/paper/smaformer-synergistic-multi-attention","slug":"smaformer-synergistic-multi-attention","title":"SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation","date":"2024-08-31","arxiv_id":"2409.00346","repositories_listed":2,"syntology":null},{"url":"/paper/mambamim-pre-training-mamba-with-state-space","slug":"mambamim-pre-training-mamba-with-state-space","title":"MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation","date":"2024-08-15","arxiv_id":"2408.08070","repositories_listed":2,"syntology":null},{"url":"/paper/2408-00874","slug":"2408-00874","title":"Medical SAM 2: Segment medical images as video via Segment Anything Model 2","date":"2024-08-01","arxiv_id":"2408.00874","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/2408-00874#ran","syntology_url":"https://syntology.ai/paper/2408.00874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.00874"}},"official":{"repos":["medicinetoken/medical-sam2"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/2407-21266","slug":"2407-21266","title":"DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs","date":"2024-07-31","arxiv_id":"2407.21266","repositories_listed":2,"syntology":null},{"url":"/paper/segment-anything-for-videos-a-systematic","slug":"segment-anything-for-videos-a-systematic","title":"Segment Anything for Videos: A Systematic Survey","date":"2024-07-31","arxiv_id":"2408.08315","repositories_listed":2,"syntology":null},{"url":"/paper/are-vision-xlstm-embedded-unet-more-reliable","slug":"are-vision-xlstm-embedded-unet-more-reliable","title":"Are Vision xLSTM Embedded UNet More Reliable in Medical 3D Image Segmentation?","date":"2024-06-24","arxiv_id":"2406.16993","repositories_listed":2,"syntology":null},{"url":"/paper/u-kan-makes-strong-backbone-for-medical-image","slug":"u-kan-makes-strong-backbone-for-medical-image","title":"U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation","date":"2024-06-05","arxiv_id":"2406.02918","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/u-kan-makes-strong-backbone-for-medical-image#ran","syntology_url":"https://syntology.ai/paper/2406.02918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02918"}},"official":null}},{"url":"/paper/abanet-attention-boundary-aware-network-for","slug":"abanet-attention-boundary-aware-network-for","title":"ABANet: Attention boundary-aware network for image segmentation","date":"2024-05-17","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/vlsm-adapter-finetuning-vision-language","slug":"vlsm-adapter-finetuning-vision-language","title":"VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks","date":"2024-05-10","arxiv_id":"2405.06196","repositories_listed":2,"syntology":null},{"url":"/paper/segformer3d-an-efficient-transformer-for-3d","slug":"segformer3d-an-efficient-transformer-for-3d","title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation","date":"2024-04-15","arxiv_id":"2404.10156","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/segformer3d-an-efficient-transformer-for-3d#ran","syntology_url":"https://syntology.ai/paper/2404.10156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10156"}},"official":{"repos":["osupcvlab/segformer3d"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-modal-conditioned-reconstruction-for","slug":"cross-modal-conditioned-reconstruction-for","title":"Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation","date":"2024-04-03","arxiv_id":"2404.02845","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cross-modal-conditioned-reconstruction-for#ran","syntology_url":"https://syntology.ai/paper/2404.02845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02845"}},"official":{"repos":["shashankhuang/reclmis","shawnhuang497/reclmis"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/concatenate-fine-tuning-re-training-a-sam","slug":"concatenate-fine-tuning-re-training-a-sam","title":"Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation","date":"2024-03-17","arxiv_id":"2403.11229","repositories_listed":2,"syntology":null},{"url":"/paper/cuvler-enhanced-unsupervised-object","slug":"cuvler-enhanced-unsupervised-object","title":"CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers","date":"2024-03-12","arxiv_id":"2403.07700","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cuvler-enhanced-unsupervised-object#ran","syntology_url":"https://syntology.ai/paper/2403.07700","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07700"}},"official":{"repos":["shahaf-arica/cuvler"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/large-window-based-mamba-unet-for-medical","slug":"large-window-based-mamba-unet-for-medical","title":"LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation","date":"2024-03-12","arxiv_id":"2403.07332","repositories_listed":2,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":11,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/large-window-based-mamba-unet-for-medical#ran","syntology_url":"https://syntology.ai/paper/2403.07332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07332"}},"official":{"repos":["wjh892521292/lkm-unet","wjh892521292/lma-unet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedfms-exploring-federated-foundation-models","slug":"fedfms-exploring-federated-foundation-models","title":"FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation","date":"2024-03-08","arxiv_id":"2403.05408","repositories_listed":2,"syntology":null},{"url":"/paper/weighted-monte-carlo-augmented-spherical","slug":"weighted-monte-carlo-augmented-spherical","title":"Efficient 3D affinely equivariant CNNs with adaptive fusion of augmented spherical Fourier-Bessel bases","date":"2024-02-26","arxiv_id":"2402.16825","repositories_listed":2,"syntology":null},{"url":"/paper/perceiving-longer-sequences-with-bi","slug":"perceiving-longer-sequences-with-bi","title":"Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers","date":"2024-02-19","arxiv_id":"2402.12138","repositories_listed":2,"syntology":{"n":28,"n_ran":17,"n_constructed":11,"n_ran_checked":11,"n_instrument":6,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":27,"phrase":"17 ran (of which 11 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 6 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/perceiving-longer-sequences-with-bi#ran","syntology_url":"https://syntology.ai/paper/2402.12138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12138"}},"official":{"repos":["mrkshllr/bixt"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":10,"n_ran_no_instrument_failure":10,"n_unverified":11,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/weak-mamba-unet-visual-mamba-makes-cnn-and","slug":"weak-mamba-unet-visual-mamba-makes-cnn-and","title":"Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation","date":"2024-02-16","arxiv_id":"2402.10887","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/weak-mamba-unet-visual-mamba-makes-cnn-and#ran","syntology_url":"https://syntology.ai/paper/2402.10887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10887"}},"official":{"repos":["ziyangwang007/mamba-unet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/unsupervised-universal-image-segmentation","slug":"unsupervised-universal-image-segmentation","title":"Unsupervised Universal Image Segmentation","date":"2023-12-28","arxiv_id":"2312.17243","repositories_listed":2,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":6,"n_instrument":7,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-universal-image-segmentation#ran","syntology_url":"https://syntology.ai/paper/2312.17243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17243"}},"official":{"repos":["u2seg/u2seg"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/pulaski-learning-inter-rater-variability","slug":"pulaski-learning-inter-rater-variability","title":"PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation","date":"2023-12-25","arxiv_id":"2312.15686","repositories_listed":2,"syntology":null},{"url":"/paper/uniref-segment-every-reference-object-in","slug":"uniref-segment-every-reference-object-in","title":"UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces","date":"2023-12-25","arxiv_id":"2312.15715","repositories_listed":2,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/uniref-segment-every-reference-object-in#ran","syntology_url":"https://syntology.ai/paper/2312.15715","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.15715"}},"official":{"repos":["foundationvision/uniref"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-multispectral-segmentation-with","slug":"few-shot-multispectral-segmentation-with","title":"Few-shot Multispectral Segmentation with Representations Generated by Reinforcement Learning","date":"2023-11-20","arxiv_id":"2311.11827","repositories_listed":2,"syntology":null},{"url":"/paper/slide-sam-medical-sam-meets-sliding-window","slug":"slide-sam-medical-sam-meets-sliding-window","title":"Slide-SAM: Medical SAM Meets Sliding Window","date":"2023-11-16","arxiv_id":"2311.10121","repositories_listed":2,"syntology":null},{"url":"/paper/iodeep-an-iod-for-the-introduction-of-deep","slug":"iodeep-an-iod-for-the-introduction-of-deep","title":"IODeep: an IOD for the introduction of deep learning in the DICOM standard","date":"2023-11-10","arxiv_id":"2311.16163","repositories_listed":2,"syntology":null},{"url":"/paper/medical-image-segmentation-with-domain","slug":"medical-image-segmentation-with-domain","title":"Medical Image Segmentation with Domain Adaptation: A Survey","date":"2023-11-03","arxiv_id":"2311.01702","repositories_listed":2,"syntology":null},{"url":"/paper/using-duck-net-for-polyp-image-segmentation-1","slug":"using-duck-net-for-polyp-image-segmentation-1","title":"Using DUCK-Net for Polyp Image Segmentation","date":"2023-11-03","arxiv_id":"2311.02239","repositories_listed":2,"syntology":null},{"url":"/paper/llava-interactive-an-all-in-one-demo-for","slug":"llava-interactive-an-all-in-one-demo-for","title":"LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing","date":"2023-11-01","arxiv_id":"2311.00571","repositories_listed":2,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/llava-interactive-an-all-in-one-demo-for#ran","syntology_url":"https://syntology.ai/paper/2311.00571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.00571"}},"official":null}},{"url":"/paper/sam-octa-prompting-segment-anything-for-octa","slug":"sam-octa-prompting-segment-anything-for-octa","title":"SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation","date":"2023-10-11","arxiv_id":"2310.07183","repositories_listed":2,"syntology":null},{"url":"/paper/a-simple-and-robust-framework-for-cross","slug":"a-simple-and-robust-framework-for-cross","title":"A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers","date":"2023-10-09","arxiv_id":"2310.05572","repositories_listed":2,"syntology":null},{"url":"/paper/free-discontinuity-design-with-an-application","slug":"free-discontinuity-design-with-an-application","title":"Free Discontinuity Regression: With an Application to the Economic Effects of Internet Shutdowns","date":"2023-09-26","arxiv_id":"2309.14630","repositories_listed":2,"syntology":null},{"url":"/paper/sam-octa-a-fine-tuning-strategy-for-applying","slug":"sam-octa-a-fine-tuning-strategy-for-applying","title":"SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation Tasks","date":"2023-09-21","arxiv_id":"2309.11758","repositories_listed":2,"syntology":null},{"url":"/paper/sam3d-segment-anything-model-in-volumetric","slug":"sam3d-segment-anything-model-in-volumetric","title":"SAM3D: Segment Anything Model in Volumetric Medical Images","date":"2023-09-07","arxiv_id":"2309.03493","repositories_listed":2,"syntology":null},{"url":"/paper/genselfdiff-his-generative-self-supervision","slug":"genselfdiff-his-generative-self-supervision","title":"GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation","date":"2023-09-04","arxiv_id":"2309.01487","repositories_listed":2,"syntology":null},{"url":"/paper/unlocking-fine-grained-details-with-wavelet","slug":"unlocking-fine-grained-details-with-wavelet","title":"Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers","date":"2023-08-25","arxiv_id":"2308.13442","repositories_listed":2,"syntology":null},{"url":"/paper/dynamic-open-vocabulary-enhanced-safe-landing","slug":"dynamic-open-vocabulary-enhanced-safe-landing","title":"Dynamic Open Vocabulary Enhanced Safe-landing with Intelligence (DOVESEI)","date":"2023-08-22","arxiv_id":"2308.11471","repositories_listed":2,"syntology":null},{"url":"/paper/polyp-sam-can-a-text-guided-sam-perform","slug":"polyp-sam-can-a-text-guided-sam-perform","title":"Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?","date":"2023-08-12","arxiv_id":"2308.06623","repositories_listed":2,"syntology":null},{"url":"/paper/cats-v2-hybrid-encoders-for-robust-medical","slug":"cats-v2-hybrid-encoders-for-robust-medical","title":"CATS v2: Hybrid encoders for robust medical segmentation","date":"2023-08-11","arxiv_id":"2308.06377","repositories_listed":2,"syntology":null},{"url":"/paper/cmunext-an-efficient-medical-image","slug":"cmunext-an-efficient-medical-image","title":"CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion","date":"2023-08-02","arxiv_id":"2308.01239","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cmunext-an-efficient-medical-image#ran","syntology_url":"https://syntology.ai/paper/2308.01239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01239"}},"official":{"repos":["fenghetan9/cmunext","FengheTan9/Medical-Image-Segmentation-Benchmarks"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/training-free-instance-segmentation-from","slug":"training-free-instance-segmentation-from","title":"Synthetic Instance Segmentation from Semantic Image Segmentation Masks","date":"2023-08-02","arxiv_id":"2308.00949","repositories_listed":2,"syntology":null},{"url":"/paper/image-segmentation-keras-implementation-of","slug":"image-segmentation-keras-implementation-of","title":"Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras","date":"2023-07-25","arxiv_id":"2307.13215","repositories_listed":2,"syntology":null},{"url":"/paper/frequency-domain-adversarial-training-for","slug":"frequency-domain-adversarial-training-for","title":"Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation","date":"2023-07-14","arxiv_id":"2307.07269","repositories_listed":2,"syntology":null},{"url":"/paper/mdvit-multi-domain-vision-transformer-for","slug":"mdvit-multi-domain-vision-transformer-for","title":"MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets","date":"2023-07-05","arxiv_id":"2307.02100","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-for-background-replacement-in","slug":"deep-learning-for-background-replacement-in","title":"Deep learning for Background Replacement in Video Conferencing","date":"2023-06-12","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/vpuformer-visual-prompt-unified-transformer","slug":"vpuformer-visual-prompt-unified-transformer","title":"PVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation","date":"2023-06-11","arxiv_id":"2306.06656","repositories_listed":2,"syntology":null},{"url":"/paper/2detect-a-large-2d-expandable-trainable","slug":"2detect-a-large-2d-expandable-trainable","title":"2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning","date":"2023-06-09","arxiv_id":"2306.05907","repositories_listed":2,"syntology":null},{"url":"/paper/training-like-a-medical-resident-universal","slug":"training-like-a-medical-resident-universal","title":"Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation","date":"2023-06-04","arxiv_id":"2306.02416","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/training-like-a-medical-resident-universal#ran","syntology_url":"https://syntology.ai/paper/2306.02416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02416"}},"official":{"repos":["yhygao/universal-medical-image-segmentation"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/nextou-efficient-topology-aware-u-net-for","slug":"nextou-efficient-topology-aware-u-net-for","title":"NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation","date":"2023-05-25","arxiv_id":"2305.15911","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nextou-efficient-topology-aware-u-net-for#ran","syntology_url":"https://syntology.ai/paper/2305.15911","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15911"}},"official":{"repos":["pengchengshi1220/nextou"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/acct-is-a-fast-and-accessible-automatic-cell","slug":"acct-is-a-fast-and-accessible-automatic-cell","title":"ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation","date":"2023-05-22","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/maxvit-unet-multi-axis-attention-for-medical","slug":"maxvit-unet-multi-axis-attention-for-medical","title":"MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation","date":"2023-05-15","arxiv_id":"2305.08396","repositories_listed":2,"syntology":null},{"url":"/paper/bidirectional-copy-paste-for-semi-supervised","slug":"bidirectional-copy-paste-for-semi-supervised","title":"Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation","date":"2023-05-01","arxiv_id":"2305.00673","repositories_listed":2,"syntology":{"n":19,"n_ran":17,"n_constructed":2,"n_ran_checked":15,"n_instrument":2,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"17 ran (of which 2 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 0 violated, 13 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bidirectional-copy-paste-for-semi-supervised#ran","syntology_url":"https://syntology.ai/paper/2305.00673","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00673"}},"official":{"repos":["HiLab-git/SSL4MIS","deepmed-lab-ecnu/bcp"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":2,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/segment-anything-model-for-medical-image","slug":"segment-anything-model-for-medical-image","title":"Segment Anything Model for Medical Image Analysis: an Experimental Study","date":"2023-04-20","arxiv_id":"2304.10517","repositories_listed":2,"syntology":null},{"url":"/paper/compete-to-win-enhancing-pseudo-labels-for","slug":"compete-to-win-enhancing-pseudo-labels-for","title":"Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation","date":"2023-04-15","arxiv_id":"2304.07519","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/compete-to-win-enhancing-pseudo-labels-for#ran","syntology_url":"https://syntology.ai/paper/2304.07519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.07519"}},"official":{"repos":["HiLab-git/SSL4MIS","huiimin5/comwin"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/universeg-universal-medical-image","slug":"universeg-universal-medical-image","title":"UniverSeg: Universal Medical Image Segmentation","date":"2023-04-12","arxiv_id":"2304.06131","repositories_listed":2,"syntology":{"n":16,"n_ran":8,"n_constructed":3,"n_ran_checked":5,"n_instrument":3,"n_unverified":8,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/universeg-universal-medical-image#ran","syntology_url":"https://syntology.ai/paper/2304.06131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06131"}},"official":{"repos":["JJGO/UniverSeg"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":8,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/action-improving-semi-supervised-medical","slug":"action-improving-semi-supervised-medical","title":"ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast","date":"2023-04-05","arxiv_id":"2304.02689","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/action-improving-semi-supervised-medical#ran","syntology_url":"https://syntology.ai/paper/2304.02689","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.02689"}},"official":{"repos":["HiLab-git/SSL4MIS","charlesyou999648/action"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/inherent-consistent-learning-for-accurate","slug":"inherent-consistent-learning-for-accurate","title":"Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation","date":"2023-03-24","arxiv_id":"2303.14175","repositories_listed":2,"syntology":null},{"url":"/paper/mi-segnet-mutual-information-based-us","slug":"mi-segnet-mutual-information-based-us","title":"MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization","date":"2023-03-22","arxiv_id":"2303.12649","repositories_listed":2,"syntology":null},{"url":"/paper/m-2-snet-multi-scale-in-multi-scale","slug":"m-2-snet-multi-scale-in-multi-scale","title":"M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation","date":"2023-03-20","arxiv_id":"2303.10894","repositories_listed":2,"syntology":null},{"url":"/paper/neural-implicit-vision-language-feature","slug":"neural-implicit-vision-language-feature","title":"Neural Implicit Vision-Language Feature Fields","date":"2023-03-20","arxiv_id":"2303.10962","repositories_listed":2,"syntology":null},{"url":"/paper/object-centric-slot-diffusion-1","slug":"object-centric-slot-diffusion-1","title":"Object-Centric Slot Diffusion","date":"2023-03-20","arxiv_id":"2303.10834","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/object-centric-slot-diffusion-1#ran","syntology_url":"https://syntology.ai/paper/2303.10834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10834"}},"official":{"repos":["jindongjiang/latent-slot-diffusion"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-up-3d-kernels-with-bayesian-frequency","slug":"scaling-up-3d-kernels-with-bayesian-frequency","title":"Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation","date":"2023-03-10","arxiv_id":"2303.05785","repositories_listed":2,"syntology":null},{"url":"/paper/self-supervised-one-shot-learning-for","slug":"self-supervised-one-shot-learning-for","title":"Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images","date":"2023-03-10","arxiv_id":"2303.05639","repositories_listed":2,"syntology":null},{"url":"/paper/unleashing-text-to-image-diffusion-models-for-1","slug":"unleashing-text-to-image-diffusion-models-for-1","title":"Unleashing Text-to-Image Diffusion Models for Visual Perception","date":"2023-03-03","arxiv_id":"2303.02153","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":2,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 2 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unleashing-text-to-image-diffusion-models-for-1#ran","syntology_url":"https://syntology.ai/paper/2303.02153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.02153"}},"official":{"repos":["wl-zhao/VPD"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"133cb320e64c031a00c90289692fdf9084d7df9d01805fe52bd315fb4e818a39","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}