{"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/7","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":7,"pages_in_order":21,"rows_per_page":100,"rows":[601,700],"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/6","next":"/task/medical-image-segmentation/papers/8","papers":[{"url":"/paper/beyond-self-attention-deformable-large-kernel","slug":"beyond-self-attention-deformable-large-kernel","title":"Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation","date":"2023-08-31","arxiv_id":"2309.00121","repositories_listed":1,"syntology":null},{"url":"/paper/dual-decoder-consistency-via-pseudo-labels","slug":"dual-decoder-consistency-via-pseudo-labels","title":"Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation","date":"2023-08-31","arxiv_id":"2308.16573","repositories_listed":1,"syntology":null},{"url":"/paper/self-sampling-meta-sam-enhancing-few-shot","slug":"self-sampling-meta-sam-enhancing-few-shot","title":"Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning","date":"2023-08-31","arxiv_id":"2308.16466","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-semantic-segmentation-1","slug":"self-supervised-semantic-segmentation-1","title":"Self-supervised Semantic Segmentation: Consistency over Transformation","date":"2023-08-31","arxiv_id":"2309.00143","repositories_listed":1,"syntology":null},{"url":"/paper/a-recycling-training-strategy-for-medical","slug":"a-recycling-training-strategy-for-medical","title":"A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models","date":"2023-08-30","arxiv_id":"2308.16355","repositories_listed":1,"syntology":null},{"url":"/paper/auto-prompting-sam-for-mobile-friendly-3d","slug":"auto-prompting-sam-for-mobile-friendly-3d","title":"AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation","date":"2023-08-28","arxiv_id":"2308.14936","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/auto-prompting-sam-for-mobile-friendly-3d#ran","syntology_url":"https://syntology.ai/paper/2308.14936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14936"}},"official":{"repos":["chengyinlee/autoprosam_2024"],"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":["official"]}}},{"url":"/paper/samdsk-combining-segment-anything-model-with","slug":"samdsk-combining-segment-anything-model-with","title":"SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation","date":"2023-08-26","arxiv_id":"2308.13759","repositories_listed":1,"syntology":null},{"url":"/paper/acc-unet-a-completely-convolutional-unet","slug":"acc-unet-a-completely-convolutional-unet","title":"ACC-UNet: A Completely Convolutional UNet model for the 2020s","date":"2023-08-25","arxiv_id":"2308.13680","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-data-perturbation-and-model","slug":"rethinking-data-perturbation-and-model","title":"Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation","date":"2023-08-23","arxiv_id":"2308.11903","repositories_listed":1,"syntology":null},{"url":"/paper/bhsd-a-3d-multi-class-brain-hemorrhage","slug":"bhsd-a-3d-multi-class-brain-hemorrhage","title":"BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset","date":"2023-08-22","arxiv_id":"2308.11298","repositories_listed":1,"syntology":null},{"url":"/paper/false-negative-positive-control-for-sam-on","slug":"false-negative-positive-control-for-sam-on","title":"False Negative/Positive Control for SAM on Noisy Medical Images","date":"2023-08-20","arxiv_id":"2308.10382","repositories_listed":1,"syntology":null},{"url":"/paper/lesionmix-a-lesion-level-data-augmentation","slug":"lesionmix-a-lesion-level-data-augmentation","title":"LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation","date":"2023-08-17","arxiv_id":"2308.09026","repositories_listed":1,"syntology":null},{"url":"/paper/confidence-contours-uncertainty-aware","slug":"confidence-contours-uncertainty-aware","title":"Confidence Contours: Uncertainty-Aware Annotation for Medical Semantic Segmentation","date":"2023-08-15","arxiv_id":"2308.07528","repositories_listed":1,"syntology":null},{"url":"/paper/dsfnet-convolutional-encoder-decoder","slug":"dsfnet-convolutional-encoder-decoder","title":"DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation","date":"2023-08-15","arxiv_id":"2308.07946","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-transfer-learning-in-medical-image","slug":"exploring-transfer-learning-in-medical-image","title":"Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models","date":"2023-08-15","arxiv_id":"2308.07706","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-automatic-m-mode-echocardiography","slug":"real-time-automatic-m-mode-echocardiography","title":"Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels","date":"2023-08-15","arxiv_id":"2308.07717","repositories_listed":1,"syntology":null},{"url":"/paper/self-prompting-large-vision-models-for-few","slug":"self-prompting-large-vision-models-for-few","title":"Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation","date":"2023-08-15","arxiv_id":"2308.07624","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"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) · 4 unverified","sample_list":"/paper/self-prompting-large-vision-models-for-few#ran","syntology_url":"https://syntology.ai/paper/2308.07624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07624"}},"official":{"repos":["peteryyzhang/few-shot-self-prompt-sam"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/large-kernel-attention-for-efficient-and","slug":"large-kernel-attention-for-efficient-and","title":"Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation","date":"2023-08-14","arxiv_id":"2308.07251","repositories_listed":1,"syntology":null},{"url":"/paper/masked-diffusion-as-self-supervised","slug":"masked-diffusion-as-self-supervised","title":"Masked Diffusion as Self-supervised Representation Learner","date":"2023-08-10","arxiv_id":"2308.05695","repositories_listed":1,"syntology":null},{"url":"/paper/dimensionality-reduction-for-improving-out-of","slug":"dimensionality-reduction-for-improving-out-of","title":"Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation","date":"2023-08-07","arxiv_id":"2308.03723","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/dimensionality-reduction-for-improving-out-of#ran","syntology_url":"https://syntology.ai/paper/2308.03723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03723"}},"official":{"repos":["mckellwoodland/dimen_reduce_mahal"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-segmentation-of","slug":"weakly-supervised-segmentation-of","title":"Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet","date":"2023-08-06","arxiv_id":"2308.03001","repositories_listed":1,"syntology":null},{"url":"/paper/harder-synthetic-anomalies-to-improve-ood","slug":"harder-synthetic-anomalies-to-improve-ood","title":"Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies","date":"2023-08-02","arxiv_id":"2308.01412","repositories_listed":1,"syntology":null},{"url":"/paper/boundary-difference-over-union-loss-for","slug":"boundary-difference-over-union-loss-for","title":"Boundary Difference Over Union Loss For Medical Image Segmentation","date":"2023-08-01","arxiv_id":"2308.00220","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-for-medical-image","slug":"domain-adaptation-for-medical-image","title":"Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training","date":"2023-07-31","arxiv_id":"2307.16660","repositories_listed":1,"syntology":null},{"url":"/paper/3d-medical-image-segmentation-with-sparse","slug":"3d-medical-image-segmentation-with-sparse","title":"3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks","date":"2023-07-30","arxiv_id":"2307.16256","repositories_listed":1,"syntology":null},{"url":"/paper/an-objective-validation-of-polyp-and","slug":"an-objective-validation-of-polyp-and","title":"Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges","date":"2023-07-30","arxiv_id":"2307.16262","repositories_listed":1,"syntology":null},{"url":"/paper/scribblevc-scribble-supervised-medical-image","slug":"scribblevc-scribble-supervised-medical-image","title":"ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding","date":"2023-07-30","arxiv_id":"2307.16226","repositories_listed":1,"syntology":null},{"url":"/paper/cross-dimensional-transfer-learning-in","slug":"cross-dimensional-transfer-learning-in","title":"Cross-dimensional transfer learning in medical image segmentation with deep learning","date":"2023-07-29","arxiv_id":"2307.15872","repositories_listed":1,"syntology":null},{"url":"/paper/scale-aware-test-time-click-adaptation-for","slug":"scale-aware-test-time-click-adaptation-for","title":"Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation","date":"2023-07-28","arxiv_id":"2307.15645","repositories_listed":1,"syntology":null},{"url":"/paper/mcpa-multi-scale-cross-perceptron-attention","slug":"mcpa-multi-scale-cross-perceptron-attention","title":"MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation","date":"2023-07-27","arxiv_id":"2307.14588","repositories_listed":1,"syntology":null},{"url":"/paper/one-shot-joint-extraction-registration-and","slug":"one-shot-joint-extraction-registration-and","title":"One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data","date":"2023-07-27","arxiv_id":"2307.15198","repositories_listed":1,"syntology":null},{"url":"/paper/multi-modal-learning-with-missing-modality-1","slug":"multi-modal-learning-with-missing-modality-1","title":"Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling","date":"2023-07-26","arxiv_id":"2307.14126","repositories_listed":1,"syntology":{"n":21,"n_ran":13,"n_constructed":5,"n_ran_checked":8,"n_instrument":5,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":21,"phrase":"13 ran (of which 5 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/multi-modal-learning-with-missing-modality-1#ran","syntology_url":"https://syntology.ai/paper/2307.14126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.14126"}},"official":{"repos":["billhhh/ShaSpec"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/swinmm-masked-multi-view-with-swin","slug":"swinmm-masked-multi-view-with-swin","title":"SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation","date":"2023-07-24","arxiv_id":"2307.12591","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/swinmm-masked-multi-view-with-swin#ran","syntology_url":"https://syntology.ai/paper/2307.12591","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12591"}},"official":{"repos":["ucsc-vlaa/swinmm"],"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/swipe-efficient-and-robust-medical-image","slug":"swipe-efficient-and-robust-medical-image","title":"SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings","date":"2023-07-23","arxiv_id":"2307.12429","repositories_listed":1,"syntology":null},{"url":"/paper/colossal-a-benchmark-for-cold-start-active","slug":"colossal-a-benchmark-for-cold-start-active","title":"COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation","date":"2023-07-22","arxiv_id":"2307.12004","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/colossal-a-benchmark-for-cold-start-active#ran","syntology_url":"https://syntology.ai/paper/2307.12004","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12004"}},"official":{"repos":["medicl-vu/colossal"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"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/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/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/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/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/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/rabit-an-efficient-transformer-using","slug":"rabit-an-efficient-transformer-using","title":"RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation","date":"2023-07-12","arxiv_id":"2307.06420","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/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/boosting-multiple-sclerosis-lesion-1","slug":"boosting-multiple-sclerosis-lesion-1","title":"Boosting multiple sclerosis lesion segmentation through attention mechanism","date":"2023-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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},{"url":"/paper/rethinking-boundary-detection-in-deep","slug":"rethinking-boundary-detection-in-deep","title":"Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation","date":"2023-05-01","arxiv_id":"2305.00678","repositories_listed":1,"syntology":null},{"url":"/paper/polyp-sam-transfer-sam-for-polyp-segmentation","slug":"polyp-sam-transfer-sam-for-polyp-segmentation","title":"Polyp-SAM: Transfer SAM for Polyp Segmentation","date":"2023-04-29","arxiv_id":"2305.00293","repositories_listed":1,"syntology":null},{"url":"/paper/segment-anything-model-for-medical-images","slug":"segment-anything-model-for-medical-images","title":"Segment Anything Model for Medical Images?","date":"2023-04-28","arxiv_id":"2304.14660","repositories_listed":1,"syntology":null},{"url":"/paper/customized-segment-anything-model-for-medical","slug":"customized-segment-anything-model-for-medical","title":"Customized Segment Anything Model for Medical Image Segmentation","date":"2023-04-26","arxiv_id":"2304.13785","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":4,"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/customized-segment-anything-model-for-medical#ran","syntology_url":"https://syntology.ai/paper/2304.13785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.13785"}},"official":{"repos":["hitachinsk/samed"],"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/diffuseexpand-expanding-dataset-for-2d","slug":"diffuseexpand-expanding-dataset-for-2d","title":"DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models","date":"2023-04-26","arxiv_id":"2304.13416","repositories_listed":1,"syntology":null},{"url":"/paper/gazesam-what-you-see-is-what-you-segment","slug":"gazesam-what-you-see-is-what-you-segment","title":"GazeSAM: What You See is What You Segment","date":"2023-04-26","arxiv_id":"2304.13844","repositories_listed":1,"syntology":null},{"url":"/paper/generalist-vision-foundation-models-for","slug":"generalist-vision-foundation-models-for","title":"Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation","date":"2023-04-25","arxiv_id":"2304.12637","repositories_listed":1,"syntology":null},{"url":"/paper/dilated-unet-a-fast-and-accurate-medical","slug":"dilated-unet-a-fast-and-accurate-medical","title":"Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture","date":"2023-04-22","arxiv_id":"2304.11450","repositories_listed":1,"syntology":null},{"url":"/paper/input-augmentation-with-sam-boosting-medical","slug":"input-augmentation-with-sam-boosting-medical","title":"Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model","date":"2023-04-22","arxiv_id":"2304.11332","repositories_listed":1,"syntology":null},{"url":"/paper/fremae-fourier-transform-meets-masked","slug":"fremae-fourier-transform-meets-masked","title":"FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation","date":"2023-04-21","arxiv_id":"2304.10864","repositories_listed":1,"syntology":null},{"url":"/paper/sam-fails-to-segment-anything-sam-adapter","slug":"sam-fails-to-segment-anything-sam-adapter","title":"SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More","date":"2023-04-18","arxiv_id":"2304.09148","repositories_listed":1,"syntology":null},{"url":"/paper/automated-computed-tomography-and-magnetic","slug":"automated-computed-tomography-and-magnetic","title":"Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide","date":"2023-04-12","arxiv_id":"2304.05901","repositories_listed":1,"syntology":null},{"url":"/paper/scale-equivariant-deep-learning-for-3d-data","slug":"scale-equivariant-deep-learning-for-3d-data","title":"Scale-Equivariant Deep Learning for 3D Data","date":"2023-04-12","arxiv_id":"2304.05864","repositories_listed":1,"syntology":null}],"record_sha256":"39ce5a0ed1842bc150e7b2cdef0c3a843d113ebb602bd1fd37959ac5fe7310f5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}