{"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/12","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":12,"pages_in_order":51,"rows_per_page":100,"rows":[1101,1200],"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/papers/11","next":"/task/image-segmentation/papers/13","papers":[{"url":"/paper/dhc-dual-debiased-heterogeneous-co-training","slug":"dhc-dual-debiased-heterogeneous-co-training","title":"DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation","date":"2023-07-22","arxiv_id":"2307.11960","repositories_listed":1,"syntology":null},{"url":"/paper/flight-contrail-segmentation-via-augmented","slug":"flight-contrail-segmentation-via-augmented","title":"Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space","date":"2023-07-22","arxiv_id":"2307.12032","repositories_listed":1,"syntology":null},{"url":"/paper/pick-the-best-pre-trained-model-towards","slug":"pick-the-best-pre-trained-model-towards","title":"Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation","date":"2023-07-22","arxiv_id":"2307.11958","repositories_listed":1,"syntology":null},{"url":"/paper/prototype-driven-and-multi-expert-integrated","slug":"prototype-driven-and-multi-expert-integrated","title":"Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation","date":"2023-07-22","arxiv_id":"2307.12180","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-vision-and-language-encoders","slug":"bridging-vision-and-language-encoders","title":"Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation","date":"2023-07-21","arxiv_id":"2307.11545","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"11 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; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/bridging-vision-and-language-encoders#ran","syntology_url":"https://syntology.ai/paper/2307.11545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.11545"}},"official":{"repos":["kkakkkka/etris"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/consistency-guided-meta-learning-for","slug":"consistency-guided-meta-learning-for","title":"Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation","date":"2023-07-21","arxiv_id":"2307.11604","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-segment-from-noisy-annotations-a","slug":"learning-to-segment-from-noisy-annotations-a","title":"Learning to Segment from Noisy Annotations: A Spatial Correction Approach","date":"2023-07-21","arxiv_id":"2308.02498","repositories_listed":1,"syntology":null},{"url":"/paper/deep-spiking-unet-for-image-processing","slug":"deep-spiking-unet-for-image-processing","title":"Deep Multi-Threshold Spiking-UNet for Image Processing","date":"2023-07-20","arxiv_id":"2307.10974","repositories_listed":1,"syntology":null},{"url":"/paper/source-free-domain-adaptation-for-medical","slug":"source-free-domain-adaptation-for-medical","title":"Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning","date":"2023-07-19","arxiv_id":"2307.09769","repositories_listed":1,"syntology":null},{"url":"/paper/evaluate-fine-tuning-strategies-for-fetal","slug":"evaluate-fine-tuning-strategies-for-fetal","title":"Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-Net","date":"2023-07-18","arxiv_id":"2307.09067","repositories_listed":1,"syntology":null},{"url":"/paper/frequency-mixed-single-source-domain","slug":"frequency-mixed-single-source-domain","title":"Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation","date":"2023-07-18","arxiv_id":"2307.09005","repositories_listed":1,"syntology":null},{"url":"/paper/ege-unet-an-efficient-group-enhanced-unet-for","slug":"ege-unet-an-efficient-group-enhanced-unet-for","title":"EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation","date":"2023-07-17","arxiv_id":"2307.08473","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ege-unet-an-efficient-group-enhanced-unet-for#ran","syntology_url":"https://syntology.ai/paper/2307.08473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08473"}},"official":{"repos":["jcruan519/ege-unet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/implementation-of-a-perception-system-for","slug":"implementation-of-a-perception-system-for","title":"Implementation of a perception system for autonomous vehicles using a detection-segmentation network in SoC FPGA","date":"2023-07-17","arxiv_id":"2307.08682","repositories_listed":1,"syntology":null},{"url":"/paper/source-free-domain-adaptive-fundus-image-1","slug":"source-free-domain-adaptive-fundus-image-1","title":"Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher","date":"2023-07-14","arxiv_id":"2307.09973","repositories_listed":1,"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":3,"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/source-free-domain-adaptive-fundus-image-1#ran","syntology_url":"https://syntology.ai/paper/2307.09973","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09973"}},"official":{"repos":["lloongx/sfda-cbmt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/correlation-aware-mutual-learning-for-semi","slug":"correlation-aware-mutual-learning-for-semi","title":"Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation","date":"2023-07-12","arxiv_id":"2307.06312","repositories_listed":1,"syntology":null},{"url":"/paper/rectifying-noisy-labels-with-sequential-prior","slug":"rectifying-noisy-labels-with-sequential-prior","title":"Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation","date":"2023-07-12","arxiv_id":"2307.05898","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/rectifying-noisy-labels-with-sequential-prior#ran","syntology_url":"https://syntology.ai/paper/2307.05898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.05898"}},"official":{"repos":["beileicui/ms-tfal"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-medical-image-segmentation-based-on-multi","slug":"3d-medical-image-segmentation-based-on-multi","title":"3D Medical Image Segmentation based on multi-scale MPU-Net","date":"2023-07-11","arxiv_id":"2307.05799","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-sam-segment-and-recognize-anything","slug":"semantic-sam-segment-and-recognize-anything","title":"Semantic-SAM: Segment and Recognize Anything at Any Granularity","date":"2023-07-10","arxiv_id":"2307.04767","repositories_listed":1,"syntology":null},{"url":"/paper/ariadne-s-thread-using-text-prompts-to","slug":"ariadne-s-thread-using-text-prompts-to","title":"Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images","date":"2023-07-08","arxiv_id":"2307.03942","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-via-inter-modal","slug":"self-supervised-learning-via-inter-modal","title":"Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation","date":"2023-07-06","arxiv_id":"2307.03008","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-domain-adaptive-medical-image","slug":"semi-supervised-domain-adaptive-medical-image","title":"Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning","date":"2023-07-06","arxiv_id":"2307.02798","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-image-segmentation-with-cross","slug":"interactive-image-segmentation-with-cross","title":"Interactive Image Segmentation with Cross-Modality Vision Transformers","date":"2023-07-05","arxiv_id":"2307.02280","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-open-vocabulary-universal-image-1","slug":"hierarchical-open-vocabulary-universal-image-1","title":"Hierarchical Open-vocabulary Universal Image Segmentation","date":"2023-07-03","arxiv_id":"2307.00764","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hierarchical-open-vocabulary-universal-image-1#ran","syntology_url":"https://syntology.ai/paper/2307.00764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00764"}},"official":{"repos":["berkeley-hipie/hipie"],"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":["official"]}}},{"url":"/paper/refsam-efficiently-adapting-segmenting","slug":"refsam-efficiently-adapting-segmenting","title":"RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation","date":"2023-07-03","arxiv_id":"2307.00997","repositories_listed":1,"syntology":null},{"url":"/paper/samaug-point-prompt-augmentation-for-segment","slug":"samaug-point-prompt-augmentation-for-segment","title":"SAMAug: Point Prompt Augmentation for Segment Anything Model","date":"2023-07-03","arxiv_id":"2307.01187","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"phrase":"8 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/samaug-point-prompt-augmentation-for-segment#ran","syntology_url":"https://syntology.ai/paper/2307.01187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01187"}},"official":{"repos":["yhydhx/samaug"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mis-fm-3d-medical-image-segmentation-using","slug":"mis-fm-3d-medical-image-segmentation-using","title":"MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset","date":"2023-06-29","arxiv_id":"2306.16925","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":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) · 0 unverified","sample_list":"/paper/mis-fm-3d-medical-image-segmentation-using#ran","syntology_url":"https://syntology.ai/paper/2306.16925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16925"}},"official":{"repos":["openmedlab/mis-fm"],"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/pcdal-a-perturbation-consistency-driven","slug":"pcdal-a-perturbation-consistency-driven","title":"PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification","date":"2023-06-29","arxiv_id":"2306.16918","repositories_listed":1,"syntology":null},{"url":"/paper/the-segment-anything-model-sam-for-remote","slug":"the-segment-anything-model-sam-for-remote","title":"The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot","date":"2023-06-29","arxiv_id":"2306.16623","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-segment-anything-model-sam-for-remote#ran","syntology_url":"https://syntology.ai/paper/2306.16623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16623"}},"official":{"repos":["opengeos/segment-geospatial"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/1m-parameters-are-enough-a-lightweight-cnn","slug":"1m-parameters-are-enough-a-lightweight-cnn","title":"1M parameters are enough? A lightweight CNN-based model for medical image segmentation","date":"2023-06-28","arxiv_id":"2306.16103","repositories_listed":1,"syntology":null},{"url":"/paper/inter-rater-uncertainty-quantification-in","slug":"inter-rater-uncertainty-quantification-in","title":"Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks","date":"2023-06-28","arxiv_id":"2306.16556","repositories_listed":1,"syntology":null},{"url":"/paper/rsprompter-learning-to-prompt-for-remote","slug":"rsprompter-learning-to-prompt-for-remote","title":"RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model","date":"2023-06-28","arxiv_id":"2306.16269","repositories_listed":1,"syntology":null},{"url":"/paper/semlaps-real-time-semantic-mapping-with","slug":"semlaps-real-time-semantic-mapping-with","title":"SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation","date":"2023-06-28","arxiv_id":"2306.16585","repositories_listed":1,"syntology":null},{"url":"/paper/delving-into-crispness-guided-label","slug":"delving-into-crispness-guided-label","title":"Delving into Crispness: Guided Label Refinement for Crisp Edge Detection","date":"2023-06-27","arxiv_id":"2306.15172","repositories_listed":1,"syntology":null},{"url":"/paper/fba-net-foreground-and-background-aware","slug":"fba-net-foreground-and-background-aware","title":"FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation","date":"2023-06-27","arxiv_id":"2306.15189","repositories_listed":1,"syntology":null},{"url":"/paper/no-service-rail-surface-defect-segmentation","slug":"no-service-rail-surface-defect-segmentation","title":"No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-scale Interaction","date":"2023-06-27","arxiv_id":"2306.15442","repositories_listed":1,"syntology":null},{"url":"/paper/medlsam-localize-and-segment-anything-model","slug":"medlsam-localize-and-segment-anything-model","title":"MedLSAM: Localize and Segment Anything Model for 3D CT Images","date":"2023-06-26","arxiv_id":"2306.14752","repositories_listed":1,"syntology":null},{"url":"/paper/mutual-query-network-for-multi-modal-product","slug":"mutual-query-network-for-multi-modal-product","title":"Mutual Query Network for Multi-Modal Product Image Segmentation","date":"2023-06-26","arxiv_id":"2306.14399","repositories_listed":1,"syntology":null},{"url":"/paper/attresdu-net-medical-image-segmentation-using","slug":"attresdu-net-medical-image-segmentation-using","title":"AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net","date":"2023-06-25","arxiv_id":"2306.14255","repositories_listed":1,"syntology":null},{"url":"/paper/scribble-supervised-cell-segmentation-using","slug":"scribble-supervised-cell-segmentation-using","title":"Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization","date":"2023-06-25","arxiv_id":"2306.14136","repositories_listed":1,"syntology":null},{"url":"/paper/ualberta-at-semeval-2023-task-1-context","slug":"ualberta-at-semeval-2023-task-1-context","title":"UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation","date":"2023-06-24","arxiv_id":"2306.14067","repositories_listed":1,"syntology":null},{"url":"/paper/3dsam-adapter-holistic-adaptation-of-sam-from","slug":"3dsam-adapter-holistic-adaptation-of-sam-from","title":"3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation","date":"2023-06-23","arxiv_id":"2306.13465","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-efficiently-adapt-large-segmentation","slug":"how-to-efficiently-adapt-large-segmentation","title":"How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images","date":"2023-06-23","arxiv_id":"2306.13731","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/how-to-efficiently-adapt-large-segmentation#ran","syntology_url":"https://syntology.ai/paper/2306.13731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13731"}},"official":{"repos":["xhu248/autosam"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ladder-fine-tuning-approach-for-sam","slug":"ladder-fine-tuning-approach-for-sam","title":"Ladder Fine-tuning approach for SAM integrating complementary network","date":"2023-06-22","arxiv_id":"2306.12737","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"6 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ladder-fine-tuning-approach-for-sam#ran","syntology_url":"https://syntology.ai/paper/2306.12737","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12737"}},"official":{"repos":["11yxk/sam-lst"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dias-a-comprehensive-benchmark-for-dsa","slug":"dias-a-comprehensive-benchmark-for-dsa","title":"DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences","date":"2023-06-21","arxiv_id":"2306.12153","repositories_listed":1,"syntology":null},{"url":"/paper/fast-segment-anything","slug":"fast-segment-anything","title":"Fast Segment Anything","date":"2023-06-21","arxiv_id":"2306.12156","repositories_listed":1,"syntology":null},{"url":"/paper/towards-better-certified-segmentation-via","slug":"towards-better-certified-segmentation-via","title":"Towards Better Certified Segmentation via Diffusion Models","date":"2023-06-16","arxiv_id":"2306.09949","repositories_listed":1,"syntology":null},{"url":"/paper/annotator-consensus-prediction-for-medical","slug":"annotator-consensus-prediction-for-medical","title":"Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models","date":"2023-06-15","arxiv_id":"2306.09004","repositories_listed":1,"syntology":null},{"url":"/paper/learnable-weight-initialization-for","slug":"learnable-weight-initialization-for","title":"Learnable Weight Initialization for Volumetric Medical Image Segmentation","date":"2023-06-15","arxiv_id":"2306.09320","repositories_listed":1,"syntology":null},{"url":"/paper/tomosam-a-3d-slicer-extension-using-sam-for","slug":"tomosam-a-3d-slicer-extension-using-sam-for","title":"TomoSAM: a 3D Slicer extension using SAM for tomography segmentation","date":"2023-06-14","arxiv_id":"2306.08609","repositories_listed":1,"syntology":null},{"url":"/paper/aerialformer-multi-resolution-transformer-for","slug":"aerialformer-multi-resolution-transformer-for","title":"AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation","date":"2023-06-12","arxiv_id":"2306.06842","repositories_listed":1,"syntology":null},{"url":"/paper/topology-aware-uncertainty-for-image-1","slug":"topology-aware-uncertainty-for-image-1","title":"Topology-Aware Uncertainty for Image Segmentation","date":"2023-06-09","arxiv_id":"2306.05671","repositories_listed":1,"syntology":{"n":27,"n_ran":21,"n_constructed":4,"n_ran_checked":17,"n_instrument":4,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":7,"phrase":"21 ran (of which 4 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/topology-aware-uncertainty-for-image-1#ran","syntology_url":"https://syntology.ai/paper/2306.05671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05671"}},"official":{"repos":["Saumya-Gupta-26/struct-uncertainty"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/channel-prior-convolutional-attention-for","slug":"channel-prior-convolutional-attention-for","title":"Channel prior convolutional attention for medical image segmentation","date":"2023-06-08","arxiv_id":"2306.05196","repositories_listed":1,"syntology":null},{"url":"/paper/devil-is-in-channels-contrastive-single","slug":"devil-is-in-channels-contrastive-single","title":"Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation","date":"2023-06-08","arxiv_id":"2306.05254","repositories_listed":1,"syntology":null},{"url":"/paper/vig-unet-vision-graph-neural-networks-for","slug":"vig-unet-vision-graph-neural-networks-for","title":"ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation","date":"2023-06-08","arxiv_id":"2306.04905","repositories_listed":1,"syntology":null},{"url":"/paper/a-dataset-for-deep-learning-based-bone","slug":"a-dataset-for-deep-learning-based-bone","title":"A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty","date":"2023-06-07","arxiv_id":"2306.04579","repositories_listed":1,"syntology":null},{"url":"/paper/smrvis-point-cloud-extraction-from-3-d","slug":"smrvis-point-cloud-extraction-from-3-d","title":"SMRVIS: Point cloud extraction from 3-D ultrasound for non-destructive testing","date":"2023-06-07","arxiv_id":"2306.04668","repositories_listed":1,"syntology":null},{"url":"/paper/tec-net-vision-transformer-embrace","slug":"tec-net-vision-transformer-embrace","title":"TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation","date":"2023-06-07","arxiv_id":"2306.04086","repositories_listed":1,"syntology":null},{"url":"/paper/cit-net-convolutional-neural-networks-hand-in","slug":"cit-net-convolutional-neural-networks-hand-in","title":"CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03373","repositories_listed":1,"syntology":{"n":23,"n_ran":15,"n_constructed":15,"n_ran_checked":15,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":23,"phrase":"15 ran (of which 15 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified; every one of the 15 samples that ran constructed an object rather than computing a result","sample_list":"/paper/cit-net-convolutional-neural-networks-hand-in#ran","syntology_url":"https://syntology.ai/paper/2306.03373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03373"}},"official":{"repos":["sr0920/cit-net"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":15,"n_ran_no_instrument_failure":15,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/conditional-diffusion-models-for-weakly","slug":"conditional-diffusion-models-for-weakly","title":"Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03878","repositories_listed":1,"syntology":null},{"url":"/paper/curriculum-based-augmented-fourier-domain","slug":"curriculum-based-augmented-fourier-domain","title":"Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03511","repositories_listed":1,"syntology":null},{"url":"/paper/dformer-diffusion-guided-transformer-for","slug":"dformer-diffusion-guided-transformer-for","title":"DFormer: Diffusion-guided Transformer for Universal Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03437","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":0,"phrase":"8 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dformer-diffusion-guided-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2306.03437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03437"}},"official":{"repos":["cp3wan/dformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/instructive-feature-enhancement-for","slug":"instructive-feature-enhancement-for","title":"Instructive Feature Enhancement for Dichotomous Medical Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03497","repositories_listed":1,"syntology":null},{"url":"/paper/towards-resilient-and-secure-smart-grids","slug":"towards-resilient-and-secure-smart-grids","title":"Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach","date":"2023-06-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dual-self-distillation-of-u-shaped-networks","slug":"dual-self-distillation-of-u-shaped-networks","title":"Volumetric medical image segmentation through dual self-distillation in U-shaped networks","date":"2023-06-05","arxiv_id":"2306.03271","repositories_listed":1,"syntology":null},{"url":"/paper/sam3d-zero-shot-3d-object-detection-via","slug":"sam3d-zero-shot-3d-object-detection-via","title":"SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model","date":"2023-06-04","arxiv_id":"2306.02245","repositories_listed":1,"syntology":null},{"url":"/paper/transformer-based-annotation-bias-aware","slug":"transformer-based-annotation-bias-aware","title":"Transformer-based Annotation Bias-aware Medical Image Segmentation","date":"2023-06-02","arxiv_id":"2306.01340","repositories_listed":1,"syntology":null},{"url":"/paper/desam-decoupling-segment-anything-model-for","slug":"desam-decoupling-segment-anything-model-for","title":"DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation","date":"2023-06-01","arxiv_id":"2306.00499","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-multi-indicator-and-multi-organ","slug":"evaluation-of-multi-indicator-and-multi-organ","title":"Evaluation of Multi-indicator And Multi-organ Medical Image Segmentation Models","date":"2023-06-01","arxiv_id":"2306.00446","repositories_listed":1,"syntology":null},{"url":"/paper/pre-training-auto-generated-volumetric-shapes","slug":"pre-training-auto-generated-volumetric-shapes","title":"Pre-Training Auto-Generated Volumetric Shapes for 3D Medical Image Segmentation","date":"2023-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robust-t-loss-for-medical-image-segmentation","slug":"robust-t-loss-for-medical-image-segmentation","title":"Robust T-Loss for Medical Image Segmentation","date":"2023-06-01","arxiv_id":"2306.00753","repositories_listed":1,"syntology":null},{"url":"/paper/s-2-me-spatial-spectral-mutual-teaching-and","slug":"s-2-me-spatial-spectral-mutual-teaching-and","title":"S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation","date":"2023-06-01","arxiv_id":"2306.00451","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-framework-for-u-net-design-and-1","slug":"a-unified-framework-for-u-net-design-and-1","title":"A Unified Framework for U-Net Design and Analysis","date":"2023-05-31","arxiv_id":"2305.19638","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"7 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-unified-framework-for-u-net-design-and-1#ran","syntology_url":"https://syntology.ai/paper/2305.19638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19638"}},"official":{"repos":["fabianfalck/unet-design"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deepmerge-deep-learning-based-region-merging","slug":"deepmerge-deep-learning-based-region-merging","title":"DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation","date":"2023-05-31","arxiv_id":"2305.19787","repositories_listed":1,"syntology":null},{"url":"/paper/democratizing-pathological-image-segmentation","slug":"democratizing-pathological-image-segmentation","title":"Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning","date":"2023-05-31","arxiv_id":"2306.00047","repositories_listed":1,"syntology":null},{"url":"/paper/treasure-in-distribution-a-domain","slug":"treasure-in-distribution-a-domain","title":"Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation","date":"2023-05-31","arxiv_id":"2305.19949","repositories_listed":1,"syntology":null},{"url":"/paper/joint-optimization-of-class-specific-training","slug":"joint-optimization-of-class-specific-training","title":"Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation","date":"2023-05-30","arxiv_id":"2305.19084","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-pathological-image","slug":"semi-supervised-pathological-image","title":"Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions","date":"2023-05-30","arxiv_id":"2305.18830","repositories_listed":1,"syntology":null},{"url":"/paper/contextual-object-detection-with-multimodal","slug":"contextual-object-detection-with-multimodal","title":"Contextual Object Detection with Multimodal Large Language Models","date":"2023-05-29","arxiv_id":"2305.18279","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/contextual-object-detection-with-multimodal#ran","syntology_url":"https://syntology.ai/paper/2305.18279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18279"}},"official":{"repos":["yuhangzang/contextdet"],"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"]}}},{"url":"/paper/few-shot-rotation-invariant-aerial-image","slug":"few-shot-rotation-invariant-aerial-image","title":"Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation","date":"2023-05-29","arxiv_id":"2306.11734","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/few-shot-rotation-invariant-aerial-image#ran","syntology_url":"https://syntology.ai/paper/2306.11734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11734"}},"official":{"repos":["caoql98/frinet"],"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/aims-all-inclusive-multi-level-segmentation","slug":"aims-all-inclusive-multi-level-segmentation","title":"AIMS: All-Inclusive Multi-Level Segmentation","date":"2023-05-28","arxiv_id":"2305.17768","repositories_listed":1,"syntology":null},{"url":"/paper/detect-any-shadow-segment-anything-for-video","slug":"detect-any-shadow-segment-anything-for-video","title":"Detect Any Shadow: Segment Anything for Video Shadow Detection","date":"2023-05-26","arxiv_id":"2305.16698","repositories_listed":1,"syntology":null},{"url":"/paper/sssegmenation-an-open-source-supervised","slug":"sssegmenation-an-open-source-supervised","title":"SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch","date":"2023-05-26","arxiv_id":"2305.17091","repositories_listed":1,"syntology":null},{"url":"/paper/self-aware-and-cross-sample-prototypical","slug":"self-aware-and-cross-sample-prototypical","title":"Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation","date":"2023-05-25","arxiv_id":"2305.16214","repositories_listed":1,"syntology":null},{"url":"/paper/an-accelerated-pipeline-for-multi-label-renal","slug":"an-accelerated-pipeline-for-multi-label-renal","title":"An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level","date":"2023-05-23","arxiv_id":"2305.14566","repositories_listed":1,"syntology":null},{"url":"/paper/vdd-varied-drone-dataset-for-semantic","slug":"vdd-varied-drone-dataset-for-semantic","title":"VDD: Varied Drone Dataset for Semantic Segmentation","date":"2023-05-23","arxiv_id":"2305.13608","repositories_listed":1,"syntology":null},{"url":"/paper/restore-anything-pipeline-segment-anything","slug":"restore-anything-pipeline-segment-anything","title":"Restore Anything Pipeline: Segment Anything Meets Image Restoration","date":"2023-05-22","arxiv_id":"2305.13093","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-based-detection-of-adversarial","slug":"uncertainty-based-detection-of-adversarial","title":"Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation","date":"2023-05-22","arxiv_id":"2305.12825","repositories_listed":1,"syntology":null},{"url":"/paper/uvosam-a-mask-free-paradigm-for-unsupervised","slug":"uvosam-a-mask-free-paradigm-for-unsupervised","title":"UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model","date":"2023-05-22","arxiv_id":"2305.12659","repositories_listed":1,"syntology":null},{"url":"/paper/when-sam-meets-shadow-detection","slug":"when-sam-meets-shadow-detection","title":"When SAM Meets Shadow Detection","date":"2023-05-19","arxiv_id":"2305.11513","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptive-sim-to-real-segmentation-of","slug":"domain-adaptive-sim-to-real-segmentation-of","title":"Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs","date":"2023-05-18","arxiv_id":"2305.10883","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-the-robustness-of-deep","slug":"quantifying-the-robustness-of-deep","title":"Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning","date":"2023-05-18","arxiv_id":"2305.11347","repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-global-context-cross-consistency","slug":"multi-level-global-context-cross-consistency","title":"Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model","date":"2023-05-16","arxiv_id":"2305.09447","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-domain-gap-self-supervised-3d","slug":"bridging-the-domain-gap-self-supervised-3d","title":"Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models","date":"2023-05-15","arxiv_id":"2305.08776","repositories_listed":1,"syntology":null},{"url":"/paper/dopus-net-quality-aware-robotic-ultrasound","slug":"dopus-net-quality-aware-robotic-ultrasound","title":"DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal","date":"2023-05-15","arxiv_id":"2305.08938","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learners-for-few-shot-weakly-supervised","slug":"meta-learners-for-few-shot-weakly-supervised","title":"Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation","date":"2023-05-11","arxiv_id":"2305.06912","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-semi-supervised-learning","slug":"uncertainty-aware-semi-supervised-learning","title":"Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation","date":"2023-05-10","arxiv_id":"2305.05984","repositories_listed":1,"syntology":null},{"url":"/paper/echo-from-noise-synthetic-ultrasound-image","slug":"echo-from-noise-synthetic-ultrasound-image","title":"Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation","date":"2023-05-09","arxiv_id":"2305.05424","repositories_listed":1,"syntology":null},{"url":"/paper/adaptiveclick-clicks-aware-transformer-with","slug":"adaptiveclick-clicks-aware-transformer-with","title":"AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation","date":"2023-05-07","arxiv_id":"2305.04276","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptiveclick-clicks-aware-transformer-with#ran","syntology_url":"https://syntology.ai/paper/2305.04276","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04276"}},"official":{"repos":["lab206/adaptiveclick"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/extraction-of-volumetric-indices-from","slug":"extraction-of-volumetric-indices-from","title":"Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?","date":"2023-05-03","arxiv_id":"2305.01997","repositories_listed":1,"syntology":null},{"url":"/paper/mci-net-multi-scale-context-integrated","slug":"mci-net-multi-scale-context-integrated","title":"Mci-net: multi-scale context integrated network for liver ct image segmentation","date":"2023-05-03","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"e198e6091330d4853d6bc7fd31f6f0285842d3d544c981b4186643334f556bf3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}