{"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/lesion-segmentation/papers/7","list_of":"/task/lesion-segmentation","task":"Lesion 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":7,"rows_per_page":100,"rows":[601,640],"of":640,"counts":{"archive_papers_tagged":640,"with_a_code_link":269,"where_syntology_ran_a_sample":29,"not_listed_spam_title":0,"listed":640,"listed_where_code_ran":29,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":4,"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/lesion-segmentation","prev":"/task/lesion-segmentation/papers/6","next":null,"papers":[{"url":null,"slug":"the-effects-of-image-pre-and-post-processing","title":"The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation","date":"2018-04-30","arxiv_id":"1805.05239","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-multiple-sclerosis-lesion-in","title":"Segmentation of Multiple Sclerosis lesion in brain MR images using Fuzzy C-Means","date":"2018-04-10","arxiv_id":"1804.03282","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-using-bayesian","title":"Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation","date":"2018-04-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetric-loss-functions-and-deep-densely","title":"Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection","date":"2018-03-28","arxiv_id":"1803.11078","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-sclerosis-lesion-segmentation-from","title":"Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks","date":"2018-03-24","arxiv_id":"1803.09172","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-clinical-diagnosis-automated-stroke","title":"Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multimodal MR Image Using Convolutional Neural Network","date":"2018-03-05","arxiv_id":"1803.05848","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-unsupervised","title":"Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation","date":"2018-02-23","arxiv_id":"1802.08655","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-soft-tissue-lesion-detection-and","title":"Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network","date":"2018-02-19","arxiv_id":"1802.06865","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-weakly-supervised-deep-lesion","title":"Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST","date":"2018-01-25","arxiv_id":"1801.08614","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-automatic-algorithm-for-breast-mri","title":"Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation","date":"2017-12-14","arxiv_id":"1712.05200","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-neural-networks-for-brain","title":"Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review","date":"2017-12-11","arxiv_id":"1712.03747","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-dermoscopic-image-segmentation-with","title":"Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks","date":"2017-09-28","arxiv_id":"1709.09780","repositories_listed":0,"syntology":null},{"url":null,"slug":"skin-lesion-segmentation-u-nets-versus","title":"Skin Lesion Segmentation: U-Nets versus Clustering","date":"2017-09-27","arxiv_id":"1710.01248","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-3d-u-nets-for-biologically","title":"Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation","date":"2017-09-09","arxiv_id":"1709.02967","repositories_listed":0,"syntology":null},{"url":null,"slug":"liver-lesion-segmentation-informed-by-joint","title":"Liver lesion segmentation informed by joint liver segmentation","date":"2017-07-24","arxiv_id":"1707.07734","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-based-automatic-liver-tumor","title":"Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering","date":"2017-06-02","arxiv_id":"1706.00842","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-liver-lesion-segmentation-using-a","title":"Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method","date":"2017-04-24","arxiv_id":"1704.07239","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fully-automated-pipeline-for-detection-and","title":"A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes","date":"2017-03-19","arxiv_id":"1703.06418","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-and-local-information-based-deep","title":"Global and Local Information Based Deep Network for Skin Lesion Segmentation","date":"2017-03-16","arxiv_id":"1703.05467","repositories_listed":0,"syntology":null},{"url":null,"slug":"skin-lesion-segmentation-based-on","title":"Skin lesion segmentation based on preprocessing, thresholding and neural networks","date":"2017-03-15","arxiv_id":"1703.04845","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-by-asymmetric-image","title":"Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners","date":"2017-03-15","arxiv_id":"1703.04981","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-skin-lesion-segmentation-using-semi","title":"Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique","date":"2017-03-13","arxiv_id":"1703.04301","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-color-augmentation-techniques-for","title":"Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis","date":"2017-03-10","arxiv_id":"1703.03702","repositories_listed":0,"syntology":null},{"url":null,"slug":"lesionseg-semantic-segmentation-of-skin","title":"LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network","date":"2017-03-09","arxiv_id":"1703.03372","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmenting-dermoscopic-images","title":"Segmenting Dermoscopic Images","date":"2017-03-09","arxiv_id":"1703.03186","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-deep-learning-method-for-classification","title":"Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge","date":"2017-03-07","arxiv_id":"1703.02182","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-multi-task-deep-learning-model-for","title":"A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification","date":"2017-03-03","arxiv_id":"1703.01025","repositories_listed":0,"syntology":null},{"url":null,"slug":"skin-lesion-analysis-towards-melanoma","title":"Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network","date":"2017-03-02","arxiv_id":"1703.00577","repositories_listed":0,"syntology":null},{"url":null,"slug":"isic-2017-skin-lesion-analysis-towards","title":"ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection","date":"2017-03-01","arxiv_id":"1703.00523","repositories_listed":0,"syntology":null},{"url":null,"slug":"skin-cancer-reorganization-and-classification","title":"Skin cancer reorganization and classification with deep neural network","date":"2017-03-01","arxiv_id":"1703.00534","repositories_listed":0,"syntology":null},{"url":null,"slug":"ii-fcn-for-skin-lesion-analysis-towards","title":"II-FCN for skin lesion analysis towards melanoma detection","date":"2017-02-28","arxiv_id":"1702.08699","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatially-aware-melanoma-segmentation-using","title":"Spatially Aware Melanoma Segmentation Using Hybrid Deep Learning Techniques","date":"2017-02-26","arxiv_id":"1702.07963","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-domain-adaptation-in","title":"Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation","date":"2017-02-25","arxiv_id":"1702.07841","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-means-clustering-and-ensemble-of","title":"k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge","date":"2017-02-23","arxiv_id":"1702.07333","repositories_listed":0,"syntology":null},{"url":"/paper/skin-lesion-analysis-toward-melanoma-2","slug":"skin-lesion-analysis-toward-melanoma-2","title":"Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)","date":"2016-05-04","arxiv_id":"1605.01397","repositories_listed":0,"syntology":null},{"url":null,"slug":"deconvolutional-feature-stacking-for-weakly","title":"Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation","date":"2016-02-16","arxiv_id":"1602.04984","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-melanoma-detection-in","title":"An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning","date":"2016-01-28","arxiv_id":"1601.07843","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-volumetry-of-liver-ablation-zones","title":"Interactive Volumetry Of Liver Ablation Zones","date":"2015-10-21","arxiv_id":"1512.04582","repositories_listed":0,"syntology":null},{"url":"/paper/fully-connected-deep-structured-networks","slug":"fully-connected-deep-structured-networks","title":"Fully Connected Deep Structured Networks","date":"2015-03-09","arxiv_id":"1503.02351","repositories_listed":0,"syntology":null},{"url":null,"slug":"skincure-an-innovative-smart-phone-based","title":"Skincure: An Innovative Smart Phone-Based Application To Assist In Melanoma Early Detection And Prevention","date":"2015-01-06","arxiv_id":"1501.01075","repositories_listed":0,"syntology":null}],"record_sha256":"f1a62473e29642187d2f5b9082f441a7a3fcf630e756becc89b6c2c2bc8852fd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}