{"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/19","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":19,"pages_in_order":51,"rows_per_page":100,"rows":[1801,1900],"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/18","next":"/task/image-segmentation/papers/20","papers":[{"url":"/paper/semi-supervised-medical-image-segmentation-1","slug":"semi-supervised-medical-image-segmentation-1","title":"Semi-supervised Medical Image Segmentation through Dual-task Consistency","date":"2020-09-09","arxiv_id":"2009.04448","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":13,"n_pointer_only":11,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/semi-supervised-medical-image-segmentation-1#ran","syntology_url":"https://syntology.ai/paper/2009.04448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.04448"}},"official":{"repos":["HiLab-git/DTC"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-musically-meaningful-explanations","slug":"towards-musically-meaningful-explanations","title":"Towards Musically Meaningful Explanations Using Source Separation","date":"2020-09-04","arxiv_id":"2009.02051","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-segmentation-of-scanning-probe","slug":"improving-the-segmentation-of-scanning-probe","title":"Improving the Segmentation of Scanning Probe Microscope Images using Convolutional Neural Networks","date":"2020-08-27","arxiv_id":"2008.12371","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-representations-for-domain","slug":"disentangled-representations-for-domain","title":"Disentangled Representations for Domain-generalized Cardiac Segmentation","date":"2020-08-26","arxiv_id":"2008.11514","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adversarial-learning-for-multi-centre","slug":"domain-adversarial-learning-for-multi-centre","title":"Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation","date":"2020-08-26","arxiv_id":"2008.11776","repositories_listed":1,"syntology":null},{"url":"/paper/revphiseg-a-memory-efficient-neural-network","slug":"revphiseg-a-memory-efficient-neural-network","title":"RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation","date":"2020-08-16","arxiv_id":"2008.06999","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-using-variational","slug":"uncertainty-quantification-using-variational","title":"Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation","date":"2020-08-12","arxiv_id":"2008.07588","repositories_listed":1,"syntology":null},{"url":"/paper/central-object-segmentation-by-deep-learning","slug":"central-object-segmentation-by-deep-learning","title":"Central object segmentation by deep learning for fruits and other roundish objects","date":"2020-08-04","arxiv_id":"2008.01251","repositories_listed":1,"syntology":null},{"url":"/paper/phrasecut-language-based-image-segmentation-1","slug":"phrasecut-language-based-image-segmentation-1","title":"PhraseCut: Language-based Image Segmentation in the Wild","date":"2020-08-03","arxiv_id":"2008.01187","repositories_listed":1,"syntology":null},{"url":"/paper/forkgan-seeing-into-the-rainy-night","slug":"forkgan-seeing-into-the-rainy-night","title":"ForkGAN: Seeing into the Rainy Night","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-pay-attention-to-mistakes","slug":"learning-to-pay-attention-to-mistakes","title":"Learning To Pay Attention To Mistakes","date":"2020-07-29","arxiv_id":"2007.15131","repositories_listed":1,"syntology":null},{"url":"/paper/integrative-analysis-for-covid-19-patient","slug":"integrative-analysis-for-covid-19-patient","title":"Integrative Analysis for COVID-19 Patient Outcome Prediction","date":"2020-07-20","arxiv_id":"2007.10416","repositories_listed":1,"syntology":null},{"url":"/paper/universal-loss-reweighting-to-balance-lesion","slug":"universal-loss-reweighting-to-balance-lesion","title":"Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation","date":"2020-07-20","arxiv_id":"2007.10033","repositories_listed":1,"syntology":null},{"url":"/paper/autoregressive-unsupervised-image","slug":"autoregressive-unsupervised-image","title":"Autoregressive Unsupervised Image Segmentation","date":"2020-07-16","arxiv_id":"2007.08247","repositories_listed":1,"syntology":null},{"url":"/paper/tackling-the-problem-of-limited-data-and","slug":"tackling-the-problem-of-limited-data-and","title":"Tackling the Problem of Limited Data and Annotations in Semantic Segmentation","date":"2020-07-14","arxiv_id":"2007.07357","repositories_listed":1,"syntology":null},{"url":"/paper/on-uncertainty-estimation-in-active-learning","slug":"on-uncertainty-estimation-in-active-learning","title":"On uncertainty estimation in active learning for image segmentation","date":"2020-07-13","arxiv_id":"2007.06364","repositories_listed":1,"syntology":{"n":18,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/on-uncertainty-estimation-in-active-learning#ran","syntology_url":"https://syntology.ai/paper/2007.06364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.06364"}},"official":{"repos":["lyn1874/region_based_active_learning"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-supervised-learning-for-multilayer","slug":"semi-supervised-learning-for-multilayer","title":"Semi-supervised Learning for Aggregated Multilayer Graphs Using Diffuse Interface Methods and Fast Matrix Vector Products","date":"2020-07-10","arxiv_id":"2007.05239","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-task-driven-data-augmentation","slug":"semi-supervised-task-driven-data-augmentation","title":"Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation","date":"2020-07-09","arxiv_id":"2007.05363","repositories_listed":1,"syntology":null},{"url":"/paper/mcu-net-a-framework-towards-uncertainty","slug":"mcu-net-a-framework-towards-uncertainty","title":"MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts","date":"2020-07-08","arxiv_id":"2007.03995","repositories_listed":1,"syntology":null},{"url":"/paper/superpixel-segmentation-using-dynamic-and","slug":"superpixel-segmentation-using-dynamic-and","title":"Superpixel Segmentation using Dynamic and Iterative Spanning Forest","date":"2020-07-08","arxiv_id":"2007.04257","repositories_listed":1,"syntology":null},{"url":"/paper/scribble-based-domain-adaptation-via-co","slug":"scribble-based-domain-adaptation-via-co","title":"Scribble-based Domain Adaptation via Co-segmentation","date":"2020-07-07","arxiv_id":"2007.03632","repositories_listed":1,"syntology":null},{"url":"/paper/an-elastic-interaction-based-loss-function","slug":"an-elastic-interaction-based-loss-function","title":"An Elastic Interaction-Based Loss Function for Medical Image Segmentation","date":"2020-07-06","arxiv_id":"2007.02663","repositories_listed":1,"syntology":null},{"url":"/paper/anatomical-data-augmentation-via-fluid-based","slug":"anatomical-data-augmentation-via-fluid-based","title":"Anatomical Data Augmentation via Fluid-based Image Registration","date":"2020-07-05","arxiv_id":"2007.02447","repositories_listed":1,"syntology":null},{"url":"/paper/robust-semantic-segmentation-in-adverse-1","slug":"robust-semantic-segmentation-in-adverse-1","title":"Robust Semantic Segmentation in Adverse Weather Conditions by means of Fast Video-Sequence Segmentation","date":"2020-07-01","arxiv_id":"2007.00290","repositories_listed":1,"syntology":null},{"url":"/paper/fabric-image-representation-encoding-networks","slug":"fabric-image-representation-encoding-networks","title":"Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation","date":"2020-06-28","arxiv_id":"2006.15578","repositories_listed":1,"syntology":null},{"url":"/paper/simulation-of-brain-resection-for-cavity","slug":"simulation-of-brain-resection-for-cavity","title":"Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning","date":"2020-06-28","arxiv_id":"2006.15693","repositories_listed":1,"syntology":null},{"url":"/paper/region-of-interest-guided-supervoxel","slug":"region-of-interest-guided-supervoxel","title":"Region-of-interest guided Supervoxel Inpainting for Self-supervision","date":"2020-06-26","arxiv_id":"2006.15186","repositories_listed":1,"syntology":null},{"url":"/paper/post-dae-anatomically-plausible-segmentation","slug":"post-dae-anatomically-plausible-segmentation","title":"Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders","date":"2020-06-24","arxiv_id":"2006.13791","repositories_listed":1,"syntology":null},{"url":"/paper/realistic-adversarial-data-augmentation-for","slug":"realistic-adversarial-data-augmentation-for","title":"Realistic Adversarial Data Augmentation for MR Image Segmentation","date":"2020-06-23","arxiv_id":"2006.13322","repositories_listed":1,"syntology":null},{"url":"/paper/auxiliary-learning-by-implicit","slug":"auxiliary-learning-by-implicit","title":"Auxiliary Learning by Implicit Differentiation","date":"2020-06-22","arxiv_id":"2007.02693","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/auxiliary-learning-by-implicit#ran","syntology_url":"https://syntology.ai/paper/2007.02693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02693"}},"official":{"repos":["AvivNavon/AuxiLearn"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/hooknet-multi-resolution-convolutional-neural","slug":"hooknet-multi-resolution-convolutional-neural","title":"HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images","date":"2020-06-22","arxiv_id":"2006.12230","repositories_listed":1,"syntology":null},{"url":"/paper/lamp-large-deep-nets-with-automated-model","slug":"lamp-large-deep-nets-with-automated-model","title":"LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation","date":"2020-06-22","arxiv_id":"2006.12575","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-of-global-and-local","slug":"contrastive-learning-of-global-and-local","title":"Contrastive learning of global and local features for medical image segmentation with limited annotations","date":"2020-06-18","arxiv_id":"2006.10511","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-out-of-distribution-detection-2","slug":"unsupervised-out-of-distribution-detection-2","title":"Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation","date":"2020-06-18","arxiv_id":"2006.10712","repositories_listed":1,"syntology":null},{"url":"/paper/video-semantic-segmentation-with-distortion","slug":"video-semantic-segmentation-with-distortion","title":"Video Semantic Segmentation with Distortion-Aware Feature Correction","date":"2020-06-18","arxiv_id":"2006.10380","repositories_listed":1,"syntology":null},{"url":"/paper/cardiac-segmentation-with-strong-anatomical","slug":"cardiac-segmentation-with-strong-anatomical","title":"Cardiac Segmentation with Strong Anatomical Guarantees","date":"2020-06-15","arxiv_id":"2006.08825","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-segmentation-networks-modelling","slug":"stochastic-segmentation-networks-modelling","title":"Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty","date":"2020-06-10","arxiv_id":"2006.06015","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/stochastic-segmentation-networks-modelling#ran","syntology_url":"https://syntology.ai/paper/2006.06015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06015"}},"official":{"repos":["biomedia-mira/stochastic_segmentation_networks"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dilated-convolutions-with-lateral-inhibitions","slug":"dilated-convolutions-with-lateral-inhibitions","title":"Dilated Convolutions with Lateral Inhibitions for Semantic Image Segmentation","date":"2020-06-05","arxiv_id":"2006.03708","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-in-medical-image","slug":"uncertainty-quantification-in-medical-image","title":"Uncertainty quantification in medical image segmentation with normalizing flows","date":"2020-06-04","arxiv_id":"2006.02683","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-study-of-2d-image-segmentation","slug":"a-comparative-study-of-2d-image-segmentation","title":"A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial","date":"2020-06-01","arxiv_id":"2006.01263","repositories_listed":1,"syntology":null},{"url":"/paper/super-bpd-super-boundary-to-pixel-direction","slug":"super-bpd-super-boundary-to-pixel-direction","title":"Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation","date":"2020-05-30","arxiv_id":"2006.00303","repositories_listed":1,"syntology":null},{"url":"/paper/map-guided-curriculum-domain-adaptation-and","slug":"map-guided-curriculum-domain-adaptation-and","title":"Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation","date":"2020-05-28","arxiv_id":"2005.14553","repositories_listed":1,"syntology":null},{"url":"/paper/traditional-method-inspired-deep-neural","slug":"traditional-method-inspired-deep-neural","title":"Traditional Method Inspired Deep Neural Network for Edge Detection","date":"2020-05-28","arxiv_id":"2005.13862","repositories_listed":1,"syntology":null},{"url":"/paper/segmentation-loss-odyssey","slug":"segmentation-loss-odyssey","title":"Segmentation Loss Odyssey","date":"2020-05-27","arxiv_id":"2005.13449","repositories_listed":1,"syntology":null},{"url":"/paper/gleason-grading-of-histology-prostate-images","slug":"gleason-grading-of-histology-prostate-images","title":"Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net","date":"2020-05-22","arxiv_id":"2005.11368","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-learning-in-video-sequences","slug":"semi-supervised-learning-in-video-sequences","title":"Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation","date":"2020-05-20","arxiv_id":"2005.10266","repositories_listed":1,"syntology":null},{"url":"/paper/increasing-margin-adversarial-ima-training-to","slug":"increasing-margin-adversarial-ima-training-to","title":"Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks","date":"2020-05-19","arxiv_id":"2005.09147","repositories_listed":1,"syntology":null},{"url":"/paper/ecg-delnet-delineation-of-ambulatory","slug":"ecg-delnet-delineation-of-ambulatory","title":"ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks","date":"2020-05-11","arxiv_id":"2005.05236","repositories_listed":1,"syntology":null},{"url":"/paper/a-weighted-difference-of-anisotropic-and","slug":"a-weighted-difference-of-anisotropic-and","title":"A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation","date":"2020-05-09","arxiv_id":"2005.04401","repositories_listed":1,"syntology":null},{"url":"/paper/boundary-aware-context-neural-network-for","slug":"boundary-aware-context-neural-network-for","title":"Boundary-aware Context Neural Network for Medical Image Segmentation","date":"2020-05-03","arxiv_id":"2005.00966","repositories_listed":1,"syntology":null},{"url":"/paper/accl-adversarial-constrained-cnn-loss-for","slug":"accl-adversarial-constrained-cnn-loss-for","title":"ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation","date":"2020-05-01","arxiv_id":"2005.00328","repositories_listed":1,"syntology":null},{"url":"/paper/an-auto-encoder-strategy-for-adaptive-image","slug":"an-auto-encoder-strategy-for-adaptive-image","title":"An Auto-Encoder Strategy for Adaptive Image Segmentation","date":"2020-04-29","arxiv_id":"2004.13903","repositories_listed":1,"syntology":null},{"url":"/paper/dru-net-an-efficient-deep-convolutional","slug":"dru-net-an-efficient-deep-convolutional","title":"DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation","date":"2020-04-28","arxiv_id":"2004.13453","repositories_listed":1,"syntology":null},{"url":"/paper/fu-net-multi-class-image-segmentation-using","slug":"fu-net-multi-class-image-segmentation-using","title":"FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net","date":"2020-04-28","arxiv_id":"2004.13470","repositories_listed":1,"syntology":null},{"url":"/paper/l-co-net-learned-condensation-optimization","slug":"l-co-net-learned-condensation-optimization","title":"L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI","date":"2020-04-21","arxiv_id":"2004.11253","repositories_listed":1,"syntology":null},{"url":"/paper/a-spatially-constrained-deep-convolutional","slug":"a-spatially-constrained-deep-convolutional","title":"A Spatially Constrained Deep Convolutional Neural Network for Nerve Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate Annotations","date":"2020-04-20","arxiv_id":"2004.09443","repositories_listed":1,"syntology":null},{"url":"/paper/a-generic-ensemble-based-deep-convolutional","slug":"a-generic-ensemble-based-deep-convolutional","title":"A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation","date":"2020-04-16","arxiv_id":"2004.07995","repositories_listed":1,"syntology":null},{"url":"/paper/improving-calibration-and-out-of-distribution","slug":"improving-calibration-and-out-of-distribution","title":"Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks","date":"2020-04-12","arxiv_id":"2004.06569","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-multiple-instance-learning","slug":"weakly-supervised-multiple-instance-learning","title":"Weakly supervised multiple instance learning histopathological tumor segmentation","date":"2020-04-10","arxiv_id":"2004.05024","repositories_listed":1,"syntology":null},{"url":"/paper/harmony-search-and-otsu-based-system-for","slug":"harmony-search-and-otsu-based-system-for","title":"Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images","date":"2020-04-06","arxiv_id":"2004.03431","repositories_listed":1,"syntology":null},{"url":"/paper/a-fast-fully-octave-convolutional-neural","slug":"a-fast-fully-octave-convolutional-neural","title":"A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation","date":"2020-04-03","arxiv_id":"2004.01317","repositories_listed":1,"syntology":null},{"url":"/paper/disir-deep-image-segmentation-with","slug":"disir-deep-image-segmentation-with","title":"DISIR: Deep Image Segmentation with Interactive Refinement","date":"2020-03-31","arxiv_id":"2003.14200","repositories_listed":1,"syntology":null},{"url":"/paper/taplab-a-fast-framework-for-semantic-video","slug":"taplab-a-fast-framework-for-semantic-video","title":"TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge","date":"2020-03-30","arxiv_id":"2003.13260","repositories_listed":1,"syntology":null},{"url":"/paper/using-deep-convolutional-neural-networks-for","slug":"using-deep-convolutional-neural-networks-for","title":"Using deep convolutional neural networks for neonatal brain image segmentation","date":"2020-03-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gen-lanenet-a-generalized-and-scalable","slug":"gen-lanenet-a-generalized-and-scalable","title":"Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection","date":"2020-03-24","arxiv_id":"2003.10656","repositories_listed":1,"syntology":null},{"url":"/paper/roam-random-layer-mixup-for-semi-supervised","slug":"roam-random-layer-mixup-for-semi-supervised","title":"ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging","date":"2020-03-20","arxiv_id":"2003.09439","repositories_listed":1,"syntology":null},{"url":"/paper/livelayer-a-semi-automatic-software-program","slug":"livelayer-a-semi-automatic-software-program","title":"Livelayer: A Semi-Automatic Software Program for Segmentation of Layers and Diabetic Macular Edema in Optical Coherence Tomography Images","date":"2020-03-12","arxiv_id":"2003.05916","repositories_listed":1,"syntology":null},{"url":"/paper/lc-gan-image-to-image-translation-based-on","slug":"lc-gan-image-to-image-translation-based-on","title":"LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images","date":"2020-03-10","arxiv_id":"2003.04949","repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-context-gating-of-embedded","slug":"multi-level-context-gating-of-embedded","title":"Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation","date":"2020-03-10","arxiv_id":"2003.05056","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-segmentation-of-spinal-multiple","slug":"automatic-segmentation-of-spinal-multiple","title":"Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?","date":"2020-03-09","arxiv_id":"2003.04377","repositories_listed":1,"syntology":null},{"url":"/paper/robust-multimodal-brain-tumor-segmentation","slug":"robust-multimodal-brain-tumor-segmentation","title":"Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion","date":"2020-02-22","arxiv_id":"2002.09708","repositories_listed":1,"syntology":null},{"url":"/paper/photi-lakeice-dataset","slug":"photi-lakeice-dataset","title":"Photi-LakeIce Dataset","date":"2020-02-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/reinforced-active-learning-for-image-1","slug":"reinforced-active-learning-for-image-1","title":"Reinforced active learning for image segmentation","date":"2020-02-16","arxiv_id":"2002.06583","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-bidirectional-cross-modality","slug":"unsupervised-bidirectional-cross-modality","title":"Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation","date":"2020-02-06","arxiv_id":"2002.02255","repositories_listed":1,"syntology":null},{"url":"/paper/universal-semantic-segmentation-for-fisheye","slug":"universal-semantic-segmentation-for-fisheye","title":"Universal Semantic Segmentation for Fisheye Urban Driving Images","date":"2020-01-31","arxiv_id":"2002.03736","repositories_listed":1,"syntology":null},{"url":"/paper/an-unsupervised-learning-model-for-medical","slug":"an-unsupervised-learning-model-for-medical","title":"Medical Image Segmentation via Unsupervised Convolutional Neural Network","date":"2020-01-28","arxiv_id":"2001.10155","repositories_listed":1,"syntology":null},{"url":"/paper/opfython-a-python-inspired-optimum-path","slug":"opfython-a-python-inspired-optimum-path","title":"OPFython: A Python-Inspired Optimum-Path Forest Classifier","date":"2020-01-28","arxiv_id":"2001.10420","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-approach-to-segmentation-of","slug":"a-deep-learning-approach-to-segmentation-of","title":"A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling","date":"2020-01-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ratlesnetv2-a-fully-convolutional-network-for","slug":"ratlesnetv2-a-fully-convolutional-network-for","title":"RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation","date":"2020-01-24","arxiv_id":"2001.09138","repositories_listed":1,"syntology":null},{"url":"/paper/a-context-based-deep-learning-approach-for","slug":"a-context-based-deep-learning-approach-for","title":"A context based deep learning approach for unbalanced medical image segmentation","date":"2020-01-08","arxiv_id":"2001.02387","repositories_listed":1,"syntology":null},{"url":"/paper/unpaired-multi-modal-segmentation-via","slug":"unpaired-multi-modal-segmentation-via","title":"Unpaired Multi-modal Segmentation via Knowledge Distillation","date":"2020-01-06","arxiv_id":"2001.03111","repositories_listed":1,"syntology":null},{"url":"/paper/multi-organ-segmentation-over-partially","slug":"multi-organ-segmentation-over-partially","title":"Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction","date":"2020-01-01","arxiv_id":"2001.00208","repositories_listed":1,"syntology":null},{"url":"/paper/automated-and-network-structure-preserving","slug":"automated-and-network-structure-preserving","title":"Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics","date":"2019-12-20","arxiv_id":"1912.09978","repositories_listed":1,"syntology":null},{"url":"/paper/segmentations-leak-membership-inference","slug":"segmentations-leak-membership-inference","title":"Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation","date":"2019-12-20","arxiv_id":"1912.09685","repositories_listed":1,"syntology":null},{"url":"/paper/what-else-can-fool-deep-learning-addressing-1","slug":"what-else-can-fool-deep-learning-addressing-1","title":"What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance","date":"2019-12-15","arxiv_id":"1912.06960","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/what-else-can-fool-deep-learning-addressing-1#ran","syntology_url":"https://syntology.ai/paper/1912.06960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.06960"}},"official":{"repos":["mahmoudnafifi/WB_color_augmenter"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-learning-initializations-for-image-1","slug":"meta-learning-initializations-for-image-1","title":"Meta-Learning Initializations for Image Segmentation","date":"2019-12-13","arxiv_id":"1912.06290","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-training-of-cnn-crf-via","slug":"end-to-end-training-of-cnn-crf-via","title":"End-to-end Training of CNN-CRF via Differentiable Dual-Decomposition","date":"2019-12-06","arxiv_id":"1912.02937","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-self-supervised-denoising-for","slug":"leveraging-self-supervised-denoising-for","title":"Leveraging Self-supervised Denoising for Image Segmentation","date":"2019-11-27","arxiv_id":"1911.12239","repositories_listed":1,"syntology":null},{"url":"/paper/improving-land-cover-segmentation-across","slug":"improving-land-cover-segmentation-across","title":"Improving land cover segmentation across satellites using domain adaptation","date":"2019-11-25","arxiv_id":"1912.05000","repositories_listed":1,"syntology":null},{"url":"/paper/segmenting-medical-mri-via-recurrent-decoding","slug":"segmenting-medical-mri-via-recurrent-decoding","title":"Segmenting Medical MRI via Recurrent Decoding Cell","date":"2019-11-21","arxiv_id":"1911.09401","repositories_listed":1,"syntology":null},{"url":"/paper/kvasir-seg-a-segmented-polyp-dataset","slug":"kvasir-seg-a-segmented-polyp-dataset","title":"Kvasir-SEG: A Segmented Polyp Dataset","date":"2019-11-16","arxiv_id":"1911.07069","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-medical-image-segmentation-with","slug":"unsupervised-medical-image-segmentation-with","title":"Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps","date":"2019-11-12","arxiv_id":"1911.05140","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-the-dice-score-and-jaccard-index","slug":"optimizing-the-dice-score-and-jaccard-index","title":"Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice","date":"2019-11-05","arxiv_id":"1911.01685","repositories_listed":1,"syntology":null},{"url":"/paper/ldls-3-d-object-segmentation-through-label","slug":"ldls-3-d-object-segmentation-through-label","title":"LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images","date":"2019-10-30","arxiv_id":"1910.13955","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ldls-3-d-object-segmentation-through-label#ran","syntology_url":"https://syntology.ai/paper/1910.13955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13955"}},"official":{"repos":["brian-h-wang/LDLS"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-generalization-via-model-agnostic","slug":"domain-generalization-via-model-agnostic","title":"Domain Generalization via Model-Agnostic Learning of Semantic Features","date":"2019-10-29","arxiv_id":"1910.13580","repositories_listed":1,"syntology":null},{"url":"/paper/anchor-diffusion-for-unsupervised-video-1","slug":"anchor-diffusion-for-unsupervised-video-1","title":"Anchor Diffusion for Unsupervised Video Object Segmentation","date":"2019-10-24","arxiv_id":"1910.10895","repositories_listed":1,"syntology":null},{"url":"/paper/miscnn-a-framework-for-medical-image","slug":"miscnn-a-framework-for-medical-image","title":"MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning","date":"2019-10-21","arxiv_id":"1910.09308","repositories_listed":1,"syntology":null},{"url":"/paper/intracranial-hemorrhage-segmentation-using","slug":"intracranial-hemorrhage-segmentation-using","title":"Intracranial Hemorrhage Segmentation Using Deep Convolutional Model","date":"2019-10-18","arxiv_id":"1910.08643","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/intracranial-hemorrhage-segmentation-using#ran","syntology_url":"https://syntology.ai/paper/1910.08643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.08643"}},"official":{"repos":["Murtadha44/-Intracranial-Hemorrhage-Segmentation-Using-Deep-Convolutional-Model-U-Net-"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lung-nodule-segmentation-via-level-set","slug":"lung-nodule-segmentation-via-level-set","title":"Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation","date":"2019-10-08","arxiv_id":"1910.03191","repositories_listed":1,"syntology":null},{"url":"/paper/a-topological-loss-function-for-deep-learning","slug":"a-topological-loss-function-for-deep-learning","title":"A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology","date":"2019-10-04","arxiv_id":"1910.01877","repositories_listed":1,"syntology":null}],"record_sha256":"daa9eb7380a88391698a540215c3c414216f4f9d009423c332b7ec2127e6c2b3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}