{"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/segmentation/papers/109","list_of":"/task/segmentation","task":"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":109,"pages_in_order":131,"rows_per_page":100,"rows":[10801,10900],"of":13072,"counts":{"archive_papers_tagged":13072,"with_a_code_link":5255,"where_syntology_ran_a_sample":976,"not_listed_spam_title":0,"listed":13072,"listed_where_code_ran":976,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":838,"every_run_a_failure_of_syntologys_instrument":138,"listed_with_a_run_with_no_instrument_failure":838,"listed_every_run_a_failure_of_syntologys_instrument":138,"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/segmentation","prev":"/task/segmentation/papers/108","next":"/task/segmentation/papers/110","papers":[{"url":null,"slug":"eikonal-region-based-active-contours-for","title":"A Region-based Randers Geodesic Approach for Image Segmentation","date":"2019-12-20","arxiv_id":"1912.10122","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-multi-slice-inputs-to","title":"Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation","date":"2019-12-19","arxiv_id":"1912.09287","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adversarial-perturbation-oriented-domain","title":"An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation","date":"2019-12-18","arxiv_id":"1912.08954","repositories_listed":0,"syntology":null},{"url":"/paper/learning-to-segment-brain-anatomy-from-2d","slug":"learning-to-segment-brain-anatomy-from-2d","title":"Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data","date":"2019-12-18","arxiv_id":"1912.08364","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-weakly-supervised-video-object","title":"One-Shot Weakly Supervised Video Object Segmentation","date":"2019-12-18","arxiv_id":"1912.08936","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-focus-image-fusion-based-on-similarity","title":"Multi-focus Image Fusion Based on Similarity Characteristics","date":"2019-12-17","arxiv_id":"1912.07959","repositories_listed":0,"syntology":null},{"url":null,"slug":"automating-vitiligo-skin-lesion-segmentation","title":"Automating Vitiligo Skin Lesion Segmentation Using Convolutional Neural Networks","date":"2019-12-16","arxiv_id":"1912.08350","repositories_listed":0,"syntology":null},{"url":null,"slug":"pneumothorax-segmentation-deep-learning-image","title":"Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax","date":"2019-12-16","arxiv_id":"1912.07329","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-for-compound-figures","title":"Semantic Segmentation for Compound figures","date":"2019-12-16","arxiv_id":"1912.07142","repositories_listed":0,"syntology":null},{"url":"/paper/segthor-segmentation-of-thoracic-organs-at","slug":"segthor-segmentation-of-thoracic-organs-at","title":"SegTHOR: Segmentation of Thoracic Organs at Risk in CT images","date":"2019-12-12","arxiv_id":"1912.05950","repositories_listed":0,"syntology":null},{"url":null,"slug":"zooming-into-face-forensics-a-pixel-level","title":"Zooming into Face Forensics: A Pixel-level Analysis","date":"2019-12-12","arxiv_id":"1912.05790","repositories_listed":0,"syntology":null},{"url":null,"slug":"bionet-infusing-biomarker-prior-into-global","title":"BioNet: Infusing Biomarker Prior into Global-to-Local Network for Choroid Segmentation in Optical Coherence Tomography Images","date":"2019-12-11","arxiv_id":"1912.05090","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-self-supervised-learning-for","title":"Multimodal Self-Supervised Learning for Medical Image Analysis","date":"2019-12-11","arxiv_id":"1912.05396","repositories_listed":0,"syntology":null},{"url":null,"slug":"u-net-with-spatial-pyramid-pooling-for-drusen","title":"U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography","date":"2019-12-11","arxiv_id":"1912.05404","repositories_listed":0,"syntology":null},{"url":null,"slug":"wide-area-land-cover-mapping-with-sentinel-1","title":"Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models","date":"2019-12-11","arxiv_id":"1912.05067","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-topology-for-end-to-end-temporal","title":"A Novel Topology for End-to-end Temporal Classification and Segmentation with Recurrent Neural Network","date":"2019-12-10","arxiv_id":"1912.04784","repositories_listed":0,"syntology":null},{"url":null,"slug":"calcaneus-radiograph-analysis-system","title":"Automatic Analysis System of Calcaneus Radiograph: Rotation-Invariant Landmark Detection for Calcaneal Angle Measurement, Fracture Identification and Fracture Region Segmentation","date":"2019-12-10","arxiv_id":"1912.04536","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-segmenting-and-tracking-object","title":"Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation","date":"2019-12-10","arxiv_id":"1912.04573","repositories_listed":0,"syntology":null},{"url":null,"slug":"hr-sar-net-a-deep-neural-network-for-urban","title":"HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data","date":"2019-12-10","arxiv_id":"1912.04441","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-optimally-segment-point-clouds","title":"Learning to Optimally Segment Point Clouds","date":"2019-12-10","arxiv_id":"1912.04976","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-aware-organ-segmentation-by-predicting","title":"Shape-Aware Organ Segmentation by Predicting Signed Distance Maps","date":"2019-12-09","arxiv_id":"1912.03849","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-wasserstein-non-negative-matrix","title":"Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution","date":"2019-12-07","arxiv_id":"1912.03463","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-distance-transform-for-tubular-structure","title":"Deep Distance Transform for Tubular Structure Segmentation in CT Scans","date":"2019-12-06","arxiv_id":"1912.03383","repositories_listed":0,"syntology":null},{"url":"/paper/self-supervised-visual-terrain-classification","slug":"self-supervised-visual-terrain-classification","title":"Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning","date":"2019-12-06","arxiv_id":"1912.03227","repositories_listed":0,"syntology":null},{"url":null,"slug":"oasis-one-pass-aligned-atlas-set-for-image","title":"OASIS: One-pass aligned Atlas Set for Image Segmentation","date":"2019-12-05","arxiv_id":"1912.02417","repositories_listed":0,"syntology":null},{"url":"/paper/polytransform-deep-polygon-transformer-for","slug":"polytransform-deep-polygon-transformer-for","title":"PolyTransform: Deep Polygon Transformer for Instance Segmentation","date":"2019-12-05","arxiv_id":"1912.02801","repositories_listed":0,"syntology":null},{"url":null,"slug":"divided-we-stand-a-novel-residual-group","title":"FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation","date":"2019-12-04","arxiv_id":"1912.02079","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-video-object-segmentation-via","title":"Automatic Video Object Segmentation via Motion-Appearance-Stream Fusion and Instance-aware Segmentation","date":"2019-12-03","arxiv_id":"1912.01373","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-channel-volumetric-neural-network-for","title":"Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT","date":"2019-12-03","arxiv_id":"1912.01362","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-panoptic-segmentation-from-dense","title":"Real-Time Panoptic Segmentation from Dense Detections","date":"2019-12-03","arxiv_id":"1912.01202","repositories_listed":0,"syntology":null},{"url":null,"slug":"rgpnet-a-real-time-general-purpose-semantic","title":"RGPNet: A Real-Time General Purpose Semantic Segmentation","date":"2019-12-03","arxiv_id":"1912.01394","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-normalization-for-multi-domain","title":"Adversarial normalization for multi domain image segmentation","date":"2019-12-02","arxiv_id":"1912.00993","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-diffusion-distance-for-image","title":"Neural Diffusion Distance for Image Segmentation","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"em-net-centerline-aware-mitochondria","title":"EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network","date":"2019-11-30","arxiv_id":"1912.00201","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-of-liver-stage-malaria","title":"Image segmentation of liver stage malaria infection with spatial uncertainty sampling","date":"2019-11-30","arxiv_id":"1912.00262","repositories_listed":0,"syntology":null},{"url":"/paper/point-cloud-instance-segmentation-using","slug":"point-cloud-instance-segmentation-using","title":"Point Cloud Instance Segmentation using Probabilistic Embeddings","date":"2019-11-30","arxiv_id":"1912.00145","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-calibration-and-predictive","title":"Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation","date":"2019-11-29","arxiv_id":"1911.13273","repositories_listed":0,"syntology":null},{"url":null,"slug":"every-frame-counts-joint-learning-of-video","title":"Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow","date":"2019-11-28","arxiv_id":"1911.12739","repositories_listed":0,"syntology":null},{"url":null,"slug":"fruit-detection-segmentation-and-3d","title":"Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards","date":"2019-11-28","arxiv_id":"1911.12889","repositories_listed":0,"syntology":null},{"url":null,"slug":"land-cover-change-detection-via-semantic","title":"Land Cover Change Detection via Semantic Segmentation","date":"2019-11-28","arxiv_id":"1911.12903","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-structure-extraction-for-forms-using","title":"Document Structure Extraction using Prior based High Resolution Hierarchical Semantic Segmentation","date":"2019-11-27","arxiv_id":"1911.12170","repositories_listed":0,"syntology":null},{"url":null,"slug":"panda-panoptic-data-augmentation","title":"PanDA: Panoptic Data Augmentation","date":"2019-11-27","arxiv_id":"1911.12317","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-post-stroke-lesion-segmentation-on","title":"Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Network","date":"2019-11-25","arxiv_id":"1911.11209","repositories_listed":0,"syntology":null},{"url":null,"slug":"atlas-based-segmentations-via-semi-supervised","title":"Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations","date":"2019-11-23","arxiv_id":"1911.10417","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-meta-learning-model-for-few","title":"Differentiable Meta-learning Model for Few-shot Semantic Segmentation","date":"2019-11-23","arxiv_id":"1911.10371","repositories_listed":0,"syntology":null},{"url":null,"slug":"globally-guided-progressive-fusion-network","title":"Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation","date":"2019-11-23","arxiv_id":"1911.10360","repositories_listed":0,"syntology":null},{"url":null,"slug":"iteratively-refined-interactive-3d-medical","title":"Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning","date":"2019-11-23","arxiv_id":"1911.10334","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-symbiosis-of-attribute-prediction-and","title":"On Symbiosis of Attribute Prediction and Semantic Segmentation","date":"2019-11-23","arxiv_id":"1911.11612","repositories_listed":0,"syntology":null},{"url":null,"slug":"identify-the-cells-nuclei-based-on-the-deep","title":"Identify the cells' nuclei based on the deep learning neural network","date":"2019-11-22","arxiv_id":"1911.09830","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-of-thigh-muscle-using","title":"Semantic Segmentation of Thigh Muscle using 2.5D Deep Learning Network Trained with Limited Datasets","date":"2019-11-21","arxiv_id":"1911.09249","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-attention-networks-for-medical","title":"Hierarchical Attention Networks for Medical Image Segmentation","date":"2019-11-20","arxiv_id":"1911.08777","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-guided-instance-segmentation-for","title":"Object-Guided Instance Segmentation for Biological Images","date":"2019-11-20","arxiv_id":"1911.09199","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-defective-skulls-from-ct-data","title":"Segmentation of Defective Skulls from CT Data for Tissue Modelling","date":"2019-11-20","arxiv_id":"1911.08805","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-by-optical","title":"Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation","date":"2019-11-20","arxiv_id":"1911.09652","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-brain-tumour-segmentation-and","title":"Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction","date":"2019-11-19","arxiv_id":"1911.08483","repositories_listed":0,"syntology":null},{"url":null,"slug":"lndb-a-lung-nodule-database-on-computed","title":"LNDb: A Lung Nodule Database on Computed Tomography","date":"2019-11-19","arxiv_id":"1911.08434","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-resolution-3d-cnn-for-mri-brain-tumor","title":"Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction","date":"2019-11-19","arxiv_id":"1911.08388","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-impact-of-object-and-sub-component","title":"On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery","date":"2019-11-19","arxiv_id":"1911.08216","repositories_listed":0,"syntology":null},{"url":null,"slug":"weak-supervision-for-generating-pixel-level","title":"Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation","date":"2019-11-19","arxiv_id":"1911.09026","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-human-claustrum-segmentation-using","title":"Automated Human Claustrum Segmentation using Deep Learning Technologies","date":"2019-11-18","arxiv_id":"1911.07515","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-image-co-segmentation-a-survey","title":"Automatic Image Co-Segmentation: A Survey","date":"2019-11-18","arxiv_id":"1911.07685","repositories_listed":0,"syntology":null},{"url":null,"slug":"oriented-boxes-for-accurate-instance","title":"Oriented Boxes for Accurate Instance Segmentation","date":"2019-11-18","arxiv_id":"1911.07732","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-clustering-with-class-independent","title":"Signal Clustering with Class-independent Segmentation","date":"2019-11-18","arxiv_id":"1911.07590","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-hierarchical-complement","title":"Learning with Hierarchical Complement Objective","date":"2019-11-17","arxiv_id":"1911.07257","repositories_listed":0,"syntology":null},{"url":null,"slug":"give-me-uncertainty-an-exploration-of","title":"Give me (un)certainty -- An exploration of parameters that affect segmentation uncertainty","date":"2019-11-14","arxiv_id":"1911.06357","repositories_listed":0,"syntology":null},{"url":null,"slug":"pi-rcnn-an-efficient-multi-sensor-3d-object","title":"PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module","date":"2019-11-14","arxiv_id":"1911.06084","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-efficient-segmentation-of-electron","title":"Cost-efficient segmentation of electron microscopy images using active learning","date":"2019-11-13","arxiv_id":"1911.05548","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-clinically-available-delineations","title":"Exploiting Clinically Available Delineations for CNN-based Segmentation in Radiotherapy Treatment Planning","date":"2019-11-12","arxiv_id":"1911.04967","repositories_listed":0,"syntology":null},{"url":null,"slug":"morphological-segmentation-inside-out-1","title":"Morphological Segmentation Inside-Out","date":"2019-11-12","arxiv_id":"1911.04916","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-multi-organ-segmentation","title":"Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision","date":"2019-11-12","arxiv_id":"1911.05113","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-cardiac-image-segmentation","title":"Deep learning for cardiac image segmentation: A review","date":"2019-11-09","arxiv_id":"1911.03723","repositories_listed":0,"syntology":null},{"url":null,"slug":"faultnet-faulty-rail-valves-detection-using","title":"FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision","date":"2019-11-09","arxiv_id":"1912.04219","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-segmentation-through-a-gated-graph","title":"Building Segmentation through a Gated Graph Convolutional Neural Network with Deep Structured Feature Embedding","date":"2019-11-08","arxiv_id":"1911.03165","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigations-of-the-influences-of-a-cnns","title":"Investigations of the Influences of a CNN's Receptive Field on Segmentation of Subnuclei of Bilateral Amygdalae","date":"2019-11-07","arxiv_id":"1911.02761","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-techniques-for-biomedical","title":"Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications","date":"2019-11-06","arxiv_id":"1911.02521","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-with-soft-dice-can-lead-to-a","title":"Optimization with soft Dice can lead to a volumetric bias","date":"2019-11-06","arxiv_id":"1911.02278","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-do-we-really-need-degenerating-u-net-on","title":"What Do We Really Need? Degenerating U-Net on Retinal Vessel Segmentation","date":"2019-11-06","arxiv_id":"1911.02660","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-gradient-boosting-network-for-optic","title":"A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation","date":"2019-11-05","arxiv_id":"1911.01648","repositories_listed":0,"syntology":null},{"url":null,"slug":"scribble-based-hierarchical-weakly-supervised","title":"Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation","date":"2019-11-05","arxiv_id":"1911.02014","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-medical-image-segmentation","title":"Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations","date":"2019-11-04","arxiv_id":"1911.01218","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-co-learning-of-geometry-and","title":"Technical Report: Co-learning of geometry and semantics for online 3D mapping","date":"2019-11-04","arxiv_id":"1911.01082","repositories_listed":0,"syntology":null},{"url":null,"slug":"gland-segmentation-in-histopathological","title":"Gland Segmentation in Histopathological Images by Deep Neural Network","date":"2019-11-03","arxiv_id":"1911.00909","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-pretrained-image-classifiers-for","title":"Leveraging Pretrained Image Classifiers for Language-Based Segmentation","date":"2019-11-03","arxiv_id":"1911.00830","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-inline-analysis-of-myocardial","title":"Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning","date":"2019-11-02","arxiv_id":"1911.00625","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-for-restoration-restore-for","title":"Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditions","date":"2019-11-02","arxiv_id":"1911.00679","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-shot-panoptic-segmentation","title":"Single-Shot Panoptic Segmentation","date":"2019-11-02","arxiv_id":"1911.00764","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-hierarchical-optimization-for-multi-view","title":"3D hierarchical optimization for Multi-view depth map coding","date":"2019-11-01","arxiv_id":"1911.00376","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-iterative-multi-path-fully-convolutional","title":"An Iterative Multi‐path Fully Convolutional Neural Network for Automatic Cardiac Segmentation in Cine MR Images","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"beamseg-a-joint-model-for-multi-document","title":"BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-discourse-level-segmentation-for","title":"Exploiting Discourse-Level Segmentation for Extractive Summarization","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"our-neural-machine-translation-systems-for","title":"Our Neural Machine Translation Systems for WAT 2019","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-feature-attention-network-for-liver","title":"Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database","date":"2019-11-01","arxiv_id":"1911.00282","repositories_listed":0,"syntology":null},{"url":null,"slug":"ucsynlp-lab-machine-translation-systems-for","title":"UCSYNLP-Lab Machine Translation Systems for WAT 2019","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-prostate-zonal-segmentation-using","title":"Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention","date":"2019-10-31","arxiv_id":"1911.00127","repositories_listed":0,"syntology":null},{"url":null,"slug":"modified-u-net-mu-net-with-incorporation-of","title":"Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images","date":"2019-10-31","arxiv_id":"1911.00140","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-annotation-quality-prediction-for-semi","title":"Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles","date":"2019-10-30","arxiv_id":"1910.13988","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-domain-shift-problem-of-medical-image","title":"The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN","date":"2019-10-30","arxiv_id":"1910.13681","repositories_listed":0,"syntology":null},{"url":null,"slug":"191013348","title":"Sequential image processing methods for improving semantic video segmentation algorithms","date":"2019-10-29","arxiv_id":"1910.13348","repositories_listed":0,"syntology":null},{"url":null,"slug":"pt-resnet-perspective-transformation-based","title":"PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation","date":"2019-10-29","arxiv_id":"1910.13055","repositories_listed":0,"syntology":null},{"url":null,"slug":"resolution-independent-meshes-of-super-pixels","title":"Resolution-independent meshes of super pixels","date":"2019-10-29","arxiv_id":"1910.13323","repositories_listed":0,"syntology":null}],"record_sha256":"f8cbfab049a5f4e5cbfc46187466a02804af53b543818432f20b89d3deb65c32","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}