{"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/medical-image-segmentation/papers/10","list_of":"/task/medical-image-segmentation","task":"Medical 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":10,"pages_in_order":21,"rows_per_page":100,"rows":[901,1000],"of":2089,"counts":{"archive_papers_tagged":2089,"with_a_code_link":1080,"where_syntology_ran_a_sample":190,"not_listed_spam_title":0,"listed":2089,"listed_where_code_ran":190,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":164,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":164,"listed_every_run_a_failure_of_syntologys_instrument":26,"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/medical-image-segmentation","prev":"/task/medical-image-segmentation/papers/9","next":"/task/medical-image-segmentation/papers/11","papers":[{"url":"/paper/mixed-transformer-u-net-for-medical-image","slug":"mixed-transformer-u-net-for-medical-image","title":"Mixed Transformer U-Net For Medical Image Segmentation","date":"2021-11-08","arxiv_id":"2111.04734","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mixed-transformer-u-net-for-medical-image#ran","syntology_url":"https://syntology.ai/paper/2111.04734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.04734"}},"official":{"repos":["dootmaan/mt-unet"],"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":["official"]}}},{"url":"/paper/fast-camouflaged-object-detection-via-edge","slug":"fast-camouflaged-object-detection-via-edge","title":"Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network","date":"2021-11-05","arxiv_id":"2111.03216","repositories_listed":1,"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":1,"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/fast-camouflaged-object-detection-via-edge#ran","syntology_url":"https://syntology.ai/paper/2111.03216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.03216"}},"official":{"repos":["gewelsji/errnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/explainable-medical-image-segmentation-via","slug":"explainable-medical-image-segmentation-via","title":"Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation","date":"2021-11-02","arxiv_id":"2111.01665","repositories_listed":1,"syntology":null},{"url":"/paper/medai-transparency-in-medical-image","slug":"medai-transparency-in-medical-image","title":"MedAI: Transparency in Medical Image Segmentation","date":"2021-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/torchxrayvision-a-library-of-chest-x-ray","slug":"torchxrayvision-a-library-of-chest-x-ray","title":"TorchXRayVision: A library of chest X-ray datasets and models","date":"2021-10-31","arxiv_id":"2111.00595","repositories_listed":1,"syntology":null},{"url":"/paper/3d-oocs-learning-prostate-segmentation-with","slug":"3d-oocs-learning-prostate-segmentation-with","title":"3D-OOCS: Learning Prostate Segmentation with Inductive Bias","date":"2021-10-29","arxiv_id":"2110.15664","repositories_listed":1,"syntology":null},{"url":"/paper/distributing-deep-learning-hyperparameter","slug":"distributing-deep-learning-hyperparameter","title":"Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation","date":"2021-10-29","arxiv_id":"2110.15884","repositories_listed":1,"syntology":null},{"url":"/paper/inconsistency-aware-uncertainty-estimation","slug":"inconsistency-aware-uncertainty-estimation","title":"Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation","date":"2021-10-17","arxiv_id":"2110.08762","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/inconsistency-aware-uncertainty-estimation#ran","syntology_url":"https://syntology.ai/paper/2110.08762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.08762"}},"official":{"repos":["koncle/coranet"],"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/e1d3-u-net-for-brain-tumor-segmentation","slug":"e1d3-u-net-for-brain-tumor-segmentation","title":"E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge","date":"2021-10-06","arxiv_id":"2110.02519","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-generative-style-transfer-for","slug":"self-supervised-generative-style-transfer-for","title":"Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation","date":"2021-10-05","arxiv_id":"2110.02117","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-u-net-deep-learning-for","slug":"transfer-learning-u-net-deep-learning-for","title":"Transfer Learning U-Net Deep Learning for Lung Ultrasound Segmentation","date":"2021-10-05","arxiv_id":"2110.02196","repositories_listed":1,"syntology":null},{"url":"/paper/training-on-polar-image-transformations","slug":"training-on-polar-image-transformations","title":"Training on Polar Image Transformations Improves Biomedical Image Segmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/all-around-real-label-supervision-cyclic","slug":"all-around-real-label-supervision-cyclic","title":"All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation","date":"2021-09-28","arxiv_id":"2109.13930","repositories_listed":1,"syntology":null},{"url":"/paper/parameter-decoupling-strategy-for-semi","slug":"parameter-decoupling-strategy-for-semi","title":"Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation","date":"2021-09-20","arxiv_id":"2109.09596","repositories_listed":1,"syntology":null},{"url":"/paper/multi-encoder-parse-decoder-network-for","slug":"multi-encoder-parse-decoder-network-for","title":"Multi-encoder parse-decoder network for sequential medical image segmentation","date":"2021-09-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/domain-composition-and-attention-for-unseen","slug":"domain-composition-and-attention-for-unseen","title":"Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation","date":"2021-09-18","arxiv_id":"2109.08852","repositories_listed":1,"syntology":null},{"url":"/paper/metamedseg-volumetric-meta-learning-for-few","slug":"metamedseg-volumetric-meta-learning-for-few","title":"MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation","date":"2021-09-18","arxiv_id":"2109.09734","repositories_listed":1,"syntology":null},{"url":"/paper/missformer-an-effective-medical-image","slug":"missformer-an-effective-medical-image","title":"MISSFormer: An Effective Medical Image Segmentation Transformer","date":"2021-09-15","arxiv_id":"2109.07162","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-contrastive-learning-for","slug":"semi-supervised-contrastive-learning-for","title":"Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation","date":"2021-09-15","arxiv_id":"2109.07407","repositories_listed":1,"syntology":null},{"url":"/paper/domain-and-content-adaptive-convolution-for","slug":"domain-and-content-adaptive-convolution-for","title":"Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation","date":"2021-09-13","arxiv_id":"2109.05676","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/domain-and-content-adaptive-convolution-for#ran","syntology_url":"https://syntology.ai/paper/2109.05676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05676"}},"official":{"repos":["ShishuaiHu/DCAC"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hcdg-a-hierarchical-consistency-framework-for","slug":"hcdg-a-hierarchical-consistency-framework-for","title":"HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation","date":"2021-09-13","arxiv_id":"2109.05742","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-with-temporal-correlated","slug":"contrastive-learning-with-temporal-correlated","title":"Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays","date":"2021-09-07","arxiv_id":"2109.03233","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-foot-ulcer-segmentation-using-an","slug":"automatic-foot-ulcer-segmentation-using-an","title":"Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks","date":"2021-09-03","arxiv_id":"2109.01408","repositories_listed":1,"syntology":null},{"url":"/paper/how-reliable-are-out-of-distribution","slug":"how-reliable-are-out-of-distribution","title":"How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?","date":"2021-09-03","arxiv_id":"2109.01668","repositories_listed":1,"syntology":null},{"url":"/paper/effect-of-the-output-activation-function-on","slug":"effect-of-the-output-activation-function-on","title":"Effect of the output activation function on the probabilities and errors in medical image segmentation","date":"2021-09-02","arxiv_id":"2109.00903","repositories_listed":1,"syntology":null},{"url":"/paper/duo-segnet-adversarial-dual-views-for-semi","slug":"duo-segnet-adversarial-dual-views-for-semi","title":"Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation","date":"2021-08-25","arxiv_id":"2108.11154","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-medical-image-segmentation-based-on","slug":"efficient-medical-image-segmentation-based-on","title":"Efficient Medical Image Segmentation Based on Knowledge Distillation","date":"2021-08-23","arxiv_id":"2108.09987","repositories_listed":1,"syntology":null},{"url":"/paper/caranet-context-axial-reverse-attention","slug":"caranet-context-axial-reverse-attention","title":"CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects","date":"2021-08-16","arxiv_id":"2108.07368","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-medical-image-segmentation","slug":"weakly-supervised-medical-image-segmentation","title":"Learning to Segment Medical Images from Few-Shot Sparse Labels","date":"2021-08-12","arxiv_id":"2108.05476","repositories_listed":1,"syntology":null},{"url":"/paper/towards-to-robust-and-generalized-medical","slug":"towards-to-robust-and-generalized-medical","title":"Towards to Robust and Generalized Medical Image Segmentation Framework","date":"2021-08-09","arxiv_id":"2108.03823","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-mr-image-segmentation-with","slug":"enhancing-mr-image-segmentation-with","title":"Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation","date":"2021-08-07","arxiv_id":"2108.03429","repositories_listed":1,"syntology":null},{"url":"/paper/improving-aleatoric-uncertainty","slug":"improving-aleatoric-uncertainty","title":"Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flows","date":"2021-08-04","arxiv_id":"2108.02155","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-mask-refinement-for-few-shot","slug":"recurrent-mask-refinement-for-few-shot","title":"Recurrent Mask Refinement for Few-Shot Medical Image Segmentation","date":"2021-08-02","arxiv_id":"2108.00622","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":4,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":9,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/recurrent-mask-refinement-for-few-shot#ran","syntology_url":"https://syntology.ai/paper/2108.00622","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00622"}},"official":{"repos":["uci-cbcl/RP-Net"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-paced-contrastive-learning-for-semi","slug":"self-paced-contrastive-learning-for-semi","title":"Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels","date":"2021-07-29","arxiv_id":"2107.13741","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-study-on-colorectal-polyp","slug":"a-comprehensive-study-on-colorectal-polyp","title":"A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation","date":"2021-07-26","arxiv_id":"2107.12435","repositories_listed":1,"syntology":null},{"url":"/paper/crosslink-net-double-branch-encoder","slug":"crosslink-net-double-branch-encoder","title":"Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions","date":"2021-07-24","arxiv_id":"2107.11517","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-based-quality-assessment-and","slug":"a-deep-learning-based-quality-assessment-and","title":"A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image","date":"2021-07-22","arxiv_id":"2107.10476","repositories_listed":1,"syntology":null},{"url":"/paper/superpixel-guided-iterative-learning-from","slug":"superpixel-guided-iterative-learning-from","title":"Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation","date":"2021-07-21","arxiv_id":"2107.10100","repositories_listed":1,"syntology":null},{"url":"/paper/transattunet-multi-level-attention-guided-u","slug":"transattunet-multi-level-attention-guided-u","title":"TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation","date":"2021-07-12","arxiv_id":"2107.05274","repositories_listed":1,"syntology":null},{"url":"/paper/transclaw-u-net-claw-u-net-with-transformers","slug":"transclaw-u-net-claw-u-net-with-transformers","title":"TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentation","date":"2021-07-12","arxiv_id":"2107.05188","repositories_listed":1,"syntology":null},{"url":"/paper/a-spatial-guided-self-supervised-clustering","slug":"a-spatial-guided-self-supervised-clustering","title":"A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation","date":"2021-07-11","arxiv_id":"2107.04934","repositories_listed":1,"syntology":null},{"url":"/paper/anatomy-of-domain-shift-impact-on-u-net","slug":"anatomy-of-domain-shift-impact-on-u-net","title":"Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation","date":"2021-07-10","arxiv_id":"2107.04914","repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-federated-deep","slug":"differentially-private-federated-deep","title":"Differentially private federated deep learning for multi-site medical image segmentation","date":"2021-07-06","arxiv_id":"2107.02586","repositories_listed":1,"syntology":null},{"url":"/paper/uacanet-uncertainty-augmented-context","slug":"uacanet-uncertainty-augmented-context","title":"UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation","date":"2021-07-06","arxiv_id":"2107.02368","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/uacanet-uncertainty-augmented-context#ran","syntology_url":"https://syntology.ai/paper/2107.02368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.02368"}},"official":{"repos":["plemeri/UACANet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/utnet-a-hybrid-transformer-architecture-for","slug":"utnet-a-hybrid-transformer-architecture-for","title":"UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation","date":"2021-07-02","arxiv_id":"2107.00781","repositories_listed":1,"syntology":null},{"url":"/paper/divergentnets-medical-image-segmentation-by","slug":"divergentnets-medical-image-segmentation-by","title":"DivergentNets: Medical Image Segmentation by Network Ensemble","date":"2021-07-01","arxiv_id":"2107.00283","repositories_listed":1,"syntology":null},{"url":"/paper/bix-nas-searching-efficient-bi-directional","slug":"bix-nas-searching-efficient-bi-directional","title":"BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation","date":"2021-06-26","arxiv_id":"2106.14033","repositories_listed":1,"syntology":null},{"url":"/paper/quality-aware-memory-network-for-interactive","slug":"quality-aware-memory-network-for-interactive","title":"Quality-Aware Memory Network for Interactive Volumetric Image Segmentation","date":"2021-06-20","arxiv_id":"2106.10686","repositories_listed":1,"syntology":null},{"url":"/paper/learning-calibrated-medical-image","slug":"learning-calibrated-medical-image","title":"Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/medical-matting-a-new-perspective-on-medical","slug":"medical-matting-a-new-perspective-on-medical","title":"Medical Matting: A New Perspective on Medical Segmentation with Uncertainty","date":"2021-06-18","arxiv_id":"2106.09887","repositories_listed":1,"syntology":null},{"url":"/paper/positional-contrastive-learning-for","slug":"positional-contrastive-learning-for","title":"Positional Contrastive Learning for Volumetric Medical Image Segmentation","date":"2021-06-16","arxiv_id":"2106.09157","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/positional-contrastive-learning-for#ran","syntology_url":"https://syntology.ai/paper/2106.09157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09157"}},"official":{"repos":["dewenzeng/positional_cl"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/ds-transunet-dual-swin-transformer-u-net-for","slug":"ds-transunet-dual-swin-transformer-u-net-for","title":"DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation","date":"2021-06-12","arxiv_id":"2106.06716","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ds-transunet-dual-swin-transformer-u-net-for#ran","syntology_url":"https://syntology.ai/paper/2106.06716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06716"}},"official":{"repos":["TianBaoGe/DS-TransUNet"],"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","unlocated"]}}},{"url":"/paper/msrf-net-a-multi-scale-residual-fusion","slug":"msrf-net-a-multi-scale-residual-fusion","title":"MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation","date":"2021-05-16","arxiv_id":"2105.07451","repositories_listed":1,"syntology":null},{"url":"/paper/cfpnet-m-a-light-weight-encoder-decoder-based","slug":"cfpnet-m-a-light-weight-encoder-decoder-based","title":"CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation","date":"2021-05-10","arxiv_id":"2105.04075","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-pixel-wise-supervision-for","slug":"beyond-pixel-wise-supervision-for","title":"Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!","date":"2021-05-03","arxiv_id":"2105.00859","repositories_listed":1,"syntology":null},{"url":"/paper/ag-curesnest-a-novel-method-for-colon-polyp","slug":"ag-curesnest-a-novel-method-for-colon-polyp","title":"AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation","date":"2021-05-02","arxiv_id":"2105.00402","repositories_listed":1,"syntology":null},{"url":"/paper/every-annotation-counts-multi-label-deep","slug":"every-annotation-counts-multi-label-deep","title":"Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation","date":"2021-04-27","arxiv_id":"2104.13243","repositories_listed":1,"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":0,"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/every-annotation-counts-multi-label-deep#ran","syntology_url":"https://syntology.ai/paper/2104.13243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.13243"}},"official":{"repos":["Simael/mlds-unet"],"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/learning-fuzzy-clustering-for-spect-ct","slug":"learning-fuzzy-clustering-for-spect-ct","title":"Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks","date":"2021-04-17","arxiv_id":"2104.08623","repositories_listed":1,"syntology":null},{"url":"/paper/darcnn-domain-adaptive-region-based","slug":"darcnn-domain-adaptive-region-based","title":"DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images","date":"2021-04-03","arxiv_id":"2104.01325","repositories_listed":1,"syntology":null},{"url":"/paper/darcnn-domain-adaptive-region-based-1","slug":"darcnn-domain-adaptive-region-based-1","title":"DARCNN: Domain Adaptive Region-based Convolutional Neural Network forUnsupervised Instance Segmentation in Biomedical Images","date":"2021-04-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fanet-a-feedback-attention-network-for","slug":"fanet-a-feedback-attention-network-for","title":"FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation","date":"2021-03-31","arxiv_id":"2103.17235","repositories_listed":1,"syntology":null},{"url":"/paper/dints-differentiable-neural-network-topology","slug":"dints-differentiable-neural-network-topology","title":"DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation","date":"2021-03-29","arxiv_id":"2103.15954","repositories_listed":1,"syntology":null},{"url":"/paper/dualnorm-unet-incorporating-global-and-local","slug":"dualnorm-unet-incorporating-global-and-local","title":"CateNorm: Categorical Normalization for Robust Medical Image Segmentation","date":"2021-03-29","arxiv_id":"2103.15858","repositories_listed":1,"syntology":null},{"url":"/paper/a-location-sensitive-local-prototype-network","slug":"a-location-sensitive-local-prototype-network","title":"A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation","date":"2021-03-18","arxiv_id":"2103.10178","repositories_listed":1,"syntology":null},{"url":"/paper/margin-preserving-self-paced-contrastive","slug":"margin-preserving-self-paced-contrastive","title":"Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation","date":"2021-03-15","arxiv_id":"2103.08454","repositories_listed":1,"syntology":null},{"url":"/paper/feddg-federated-domain-generalization-on","slug":"feddg-federated-domain-generalization-on","title":"FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space","date":"2021-03-10","arxiv_id":"2103.06030","repositories_listed":1,"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/feddg-federated-domain-generalization-on#ran","syntology_url":"https://syntology.ai/paper/2103.06030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.06030"}},"official":{"repos":["liuquande/FedDG-ELCFS"],"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/boosting-semi-supervised-image-segmentation","slug":"boosting-semi-supervised-image-segmentation","title":"Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization","date":"2021-03-08","arxiv_id":"2103.04813","repositories_listed":1,"syntology":null},{"url":"/paper/dual-task-mutual-learning-for-semi-supervised","slug":"dual-task-mutual-learning-for-semi-supervised","title":"Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation","date":"2021-03-08","arxiv_id":"2103.04708","repositories_listed":1,"syntology":null},{"url":"/paper/nerd-neural-representation-of-distribution","slug":"nerd-neural-representation-of-distribution","title":"NeRD: Neural Representation of Distribution for Medical Image Segmentation","date":"2021-03-06","arxiv_id":"2103.04020","repositories_listed":1,"syntology":null},{"url":"/paper/cotr-efficiently-bridging-cnn-and-transformer","slug":"cotr-efficiently-bridging-cnn-and-transformer","title":"CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation","date":"2021-03-04","arxiv_id":"2103.03024","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-context-feedback-loop-for","slug":"learning-with-context-feedback-loop-for","title":"Learning With Context Feedback Loop for Robust Medical Image Segmentation","date":"2021-03-04","arxiv_id":"2103.02844","repositories_listed":1,"syntology":null},{"url":"/paper/convolution-free-medical-image-segmentation","slug":"convolution-free-medical-image-segmentation","title":"Convolution-Free Medical Image Segmentation using Transformers","date":"2021-02-26","arxiv_id":"2102.13645","repositories_listed":1,"syntology":null},{"url":"/paper/mixsearch-searching-for-domain-generalized","slug":"mixsearch-searching-for-domain-generalized","title":"MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures","date":"2021-02-26","arxiv_id":"2102.13280","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mixsearch-searching-for-domain-generalized#ran","syntology_url":"https://syntology.ai/paper/2102.13280","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.13280"}},"official":{"repos":["lswzjuer/NAS-WDAN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/chexseg-combining-expert-annotations-with-dnn","slug":"chexseg-combining-expert-annotations-with-dnn","title":"CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation","date":"2021-02-21","arxiv_id":"2102.10484","repositories_listed":1,"syntology":null},{"url":"/paper/squeeze-and-excitation-normalization-for","slug":"squeeze-and-excitation-normalization-for","title":"Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images","date":"2021-02-20","arxiv_id":"2102.10446","repositories_listed":1,"syntology":null},{"url":"/paper/transfuse-fusing-transformers-and-cnns-for","slug":"transfuse-fusing-transformers-and-cnns-for","title":"TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation","date":"2021-02-16","arxiv_id":"2102.08005","repositories_listed":1,"syntology":null},{"url":"/paper/reconstruction-based-membership-inference","slug":"reconstruction-based-membership-inference","title":"Membership Inference Attacks are Easier on Difficult Problems","date":"2021-02-15","arxiv_id":"2102.07762","repositories_listed":1,"syntology":null},{"url":"/paper/multi-scale-gcn-assisted-two-stage-network","slug":"multi-scale-gcn-assisted-two-stage-network","title":"Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images","date":"2021-02-09","arxiv_id":"2102.04799","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-the-probabilistic-distribution-of","slug":"modeling-the-probabilistic-distribution-of","title":"Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation","date":"2021-02-03","arxiv_id":"2102.02033","repositories_listed":1,"syntology":null},{"url":"/paper/ddanet-dual-decoder-attention-network-for","slug":"ddanet-dual-decoder-attention-network-for","title":"DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation","date":"2020-12-30","arxiv_id":"2012.15245","repositories_listed":1,"syntology":null},{"url":"/paper/pyramid-focus-augmentation-medical-image","slug":"pyramid-focus-augmentation-medical-image","title":"Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus","date":"2020-12-14","arxiv_id":"2012.07430","repositories_listed":1,"syntology":null},{"url":"/paper/aide-annotation-efficient-deep-learning-for","slug":"aide-annotation-efficient-deep-learning-for","title":"Annotation-efficient deep learning for automatic medical image segmentation","date":"2020-12-09","arxiv_id":"2012.04885","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-human-error-from-ground-truth","slug":"disentangling-human-error-from-ground-truth","title":"Disentangling Human Error from Ground Truth in Segmentation of Medical Images","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/inter-slice-context-residual-learning-for-3d","slug":"inter-slice-context-residual-learning-for-3d","title":"Inter-slice Context Residual Learning for 3D Medical Image Segmentation","date":"2020-11-28","arxiv_id":"2011.14155","repositories_listed":1,"syntology":null},{"url":"/paper/dodnet-learning-to-segment-multi-organ-and","slug":"dodnet-learning-to-segment-multi-organ-and","title":"DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets","date":"2020-11-20","arxiv_id":"2011.10217","repositories_listed":1,"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/dodnet-learning-to-segment-multi-organ-and#ran","syntology_url":"https://syntology.ai/paper/2011.10217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10217"}},"official":null}},{"url":"/paper/contrastive-registration-for-unsupervised","slug":"contrastive-registration-for-unsupervised","title":"Contrastive Registration for Unsupervised Medical Image Segmentation","date":"2020-11-17","arxiv_id":"2011.08894","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-in-magnetic-resonance-prostate","slug":"deep-learning-in-magnetic-resonance-prostate","title":"Deep learning in magnetic resonance prostate segmentation: A review and a new perspective","date":"2020-11-16","arxiv_id":"2011.07795","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-polyp-detection-localisation-and","slug":"real-time-polyp-detection-localisation-and","title":"Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning","date":"2020-11-15","arxiv_id":"2011.07631","repositories_listed":1,"syntology":null},{"url":"/paper/learning-euler-s-elastica-model-for-medical","slug":"learning-euler-s-elastica-model-for-medical","title":"Learning Euler's Elastica Model for Medical Image Segmentation","date":"2020-11-01","arxiv_id":"2011.00526","repositories_listed":1,"syntology":null},{"url":"/paper/a-teacher-student-framework-for-semi","slug":"a-teacher-student-framework-for-semi","title":"A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision","date":"2020-10-23","arxiv_id":"2010.12219","repositories_listed":1,"syntology":null},{"url":"/paper/kvasir-instrument-diagnostic-and-therapeutic","slug":"kvasir-instrument-diagnostic-and-therapeutic","title":"Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy","date":"2020-10-23","arxiv_id":"2011.08065","repositories_listed":1,"syntology":null},{"url":"/paper/selective-information-passing-for-mr-ct-image","slug":"selective-information-passing-for-mr-ct-image","title":"Selective Information Passing for MR/CT Image Segmentation","date":"2020-10-10","arxiv_id":"2010.04920","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-data-augmentation-for-3d-medical","slug":"automatic-data-augmentation-for-3d-medical","title":"Automatic Data Augmentation for 3D Medical Image Segmentation","date":"2020-10-07","arxiv_id":"2010.11695","repositories_listed":1,"syntology":null},{"url":"/paper/kiu-net-overcomplete-convolutional","slug":"kiu-net-overcomplete-convolutional","title":"KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation","date":"2020-10-04","arxiv_id":"2010.01663","repositories_listed":1,"syntology":null},{"url":"/paper/learning-non-unique-segmentation-with-reward","slug":"learning-non-unique-segmentation-with-reward","title":"Learning Non-Unique Segmentation with Reward-Penalty Dice Loss","date":"2020-09-23","arxiv_id":"2009.10987","repositories_listed":1,"syntology":null},{"url":"/paper/dual-encoder-fusion-u-net-defu-net-for-cross","slug":"dual-encoder-fusion-u-net-defu-net-for-cross","title":"Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation","date":"2020-09-11","arxiv_id":"2009.10608","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-segmentation-and-visualization-of","slug":"automatic-segmentation-and-visualization-of","title":"Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning","date":"2020-09-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/comprehensive-comparison-of-deep-learning","slug":"comprehensive-comparison-of-deep-learning","title":"Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans","date":"2020-09-10","arxiv_id":"2009.06412","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-medical-image-segmentation-1","slug":"semi-supervised-medical-image-segmentation-1","title":"Semi-supervised Medical Image Segmentation through Dual-task Consistency","date":"2020-09-09","arxiv_id":"2009.04448","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":13,"n_pointer_only":11,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/semi-supervised-medical-image-segmentation-1#ran","syntology_url":"https://syntology.ai/paper/2009.04448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.04448"}},"official":{"repos":["HiLab-git/DTC"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/revphiseg-a-memory-efficient-neural-network","slug":"revphiseg-a-memory-efficient-neural-network","title":"RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation","date":"2020-08-16","arxiv_id":"2008.06999","repositories_listed":1,"syntology":null}],"record_sha256":"ba2a5c3ed065f2c454f11764de95d1a186c99af98da97c99477efe13ded6ffce","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}