{"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/93","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":93,"pages_in_order":131,"rows_per_page":100,"rows":[9201,9300],"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/92","next":"/task/segmentation/papers/94","papers":[{"url":null,"slug":"roadobstacle21","title":"RoadObstacle21","date":"2021-10-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"colour-augmentation-for-improved-semi","title":"Colour augmentation for improved semi-supervised semantic segmentation","date":"2021-10-09","arxiv_id":"2110.04487","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-meta-segmentation-neural-network","title":"3D Meta-Segmentation Neural Network","date":"2021-10-08","arxiv_id":"2110.04297","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-trainable-video-panoptic","title":"An End-to-End Trainable Video Panoptic Segmentation Method usingTransformers","date":"2021-10-08","arxiv_id":"2110.04009","repositories_listed":0,"syntology":null},{"url":null,"slug":"dmml-net-deep-metametric-learning-for-few","title":"DMML-Net: Deep Metametric Learning for Few-Shot Geographic Object Segmentation in Remote Sensing Imagery","date":"2021-10-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"maximize-the-exploration-of-congeneric","title":"Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation","date":"2021-10-08","arxiv_id":"2110.03982","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-3d-shape-segmentation-functions","title":"Meta-Learning 3D Shape Segmentation Functions","date":"2021-10-08","arxiv_id":"2110.03854","repositories_listed":0,"syntology":null},{"url":"/paper/anoseg-anomaly-segmentation-network-using","slug":"anoseg-anomaly-segmentation-network-using","title":"AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning","date":"2021-10-07","arxiv_id":"2110.03396","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-segmentation-of-cell-nuclei-in-3-d","title":"Robust Segmentation of Cell Nuclei in 3-D Microscopy Images","date":"2021-10-07","arxiv_id":"2110.03193","repositories_listed":0,"syntology":null},{"url":null,"slug":"skullengine-a-multi-stage-cnn-framework-for","title":"SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection","date":"2021-10-07","arxiv_id":"2110.03828","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robotic-knee-arthroscopy-multi-scale","title":"Towards Robotic Knee Arthroscopy: Multi-Scale Network for Tissue-Tool Segmentation","date":"2021-10-06","arxiv_id":"2110.02657","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-instance-segmentation-with-high","title":"Deep Instance Segmentation with Automotive Radar Detection Points","date":"2021-10-05","arxiv_id":"2110.01775","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-up-instance-annotation-via-label-1","title":"Scaling up instance annotation via label propagation","date":"2021-10-05","arxiv_id":"2110.02277","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-velocity-mapping-cardiac-mri","title":"Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation","date":"2021-10-04","arxiv_id":"2110.01304","repositories_listed":0,"syntology":null},{"url":null,"slug":"weak-shot-semantic-segmentation-by","title":"Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary","date":"2021-10-04","arxiv_id":"2110.01519","repositories_listed":0,"syntology":null},{"url":null,"slug":"ear-u-net-efficientnet-and-attention-based","title":"EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT","date":"2021-10-03","arxiv_id":"2110.01014","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-segmentation-for-covid-19","title":"Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans","date":"2021-10-03","arxiv_id":"2110.00948","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-instance-segmentation-with-a","title":"3rd Place Scheme on Instance Segmentation Track of ICCV 2021 VIPriors Challenges","date":"2021-10-01","arxiv_id":"2110.00242","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-of-inter-label-geometric","title":"Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation","date":"2021-10-01","arxiv_id":"2110.00404","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-sa-la-net-a-framework-for","title":"Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images","date":"2021-10-01","arxiv_id":"2110.00682","repositories_listed":0,"syntology":null},{"url":null,"slug":"span-labeling-approach-for-vietnamese-and","title":"Span Labeling Approach for Vietnamese and Chinese Word Segmentation","date":"2021-10-01","arxiv_id":"2110.00156","repositories_listed":0,"syntology":null},{"url":null,"slug":"wideband-signal-localization-with-spectral","title":"Wideband Signal Localization with Spectral Segmentation","date":"2021-10-01","arxiv_id":"2110.00583","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-prior-knowledge-based-tumor-and-tumoral","title":"A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors","date":"2021-09-30","arxiv_id":"2109.14775","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-airway-segmentation-by-learning","title":"Automated airway segmentation by learning graphical structure","date":"2021-09-30","arxiv_id":"2109.14792","repositories_listed":0,"syntology":null},{"url":null,"slug":"bend-net-bending-loss-regularized-multitask","title":"Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images","date":"2021-09-30","arxiv_id":"2109.15283","repositories_listed":0,"syntology":null},{"url":"/paper/ishape-a-first-step-towards-irregular-shape","slug":"ishape-a-first-step-towards-irregular-shape","title":"iShape: A First Step Towards Irregular Shape Instance Segmentation","date":"2021-09-30","arxiv_id":"2109.15068","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferability-estimation-for-semantic","title":"Transferability Estimation for Semantic Segmentation Task","date":"2021-09-30","arxiv_id":"2109.15242","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compositional-approach-to-occlusion-in","title":"A Compositional Approach to Occlusion in Panoptic Segmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-generalization-for-semantic","title":"Adaptive Generalization for Semantic Segmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-semantic-segmentation-via-feature","title":"Boosting Semantic Segmentation via Feature Enhancement","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-atlas-based-and-neural-network","title":"Comparison of atlas-based and neural-network-based semantic segmentation for DENSE MRI images","date":"2021-09-29","arxiv_id":"2109.14116","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-video-language-segmentation","title":"Contrastive Video-Language Segmentation","date":"2021-09-29","arxiv_id":"2109.14131","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-and-assessing-anomaly-detectors-for","title":"Improving and Assessing Anomaly Detectors for Large-Scale Settings","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-robust-adaptive-semantic","title":"Model-Based Robust Adaptive Semantic Segmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multipath-cnn-with-alpha-matte-inference-for","title":"Multipath CNN with alpha matte inference for knee tissue segmentation from MRI","date":"2021-09-29","arxiv_id":"2109.14249","repositories_listed":0,"syntology":null},{"url":null,"slug":"resolving-label-uncertainty-with-implicit","title":"Resolving label uncertainty with implicit generative models","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segtime-precise-time-series-segmentation","title":"SegTime: Precise Time Series Segmentation without Sliding Window","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-3d-medical-image","title":"Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout","date":"2021-09-29","arxiv_id":"2109.14288","repositories_listed":0,"syntology":null},{"url":null,"slug":"should-we-replace-cnns-with-transformers-for","title":"Should we Replace CNNs with Transformers for Medical Images?","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-in-semantic-2","title":"Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training","date":"2021-09-29","arxiv_id":"2109.14219","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-via-pseudo","title":"Unsupervised Domain Adaptation Via Pseudo-labels And Objectness Constraints","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wedge-web-image-assisted-domain","title":"WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation","date":"2021-09-29","arxiv_id":"2109.14196","repositories_listed":0,"syntology":null},{"url":"/paper/warp-refine-propagation-semi-supervised-auto","slug":"warp-refine-propagation-semi-supervised-auto","title":"Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency","date":"2021-09-28","arxiv_id":"2109.13432","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-entangled-segmentation-and","title":"An End-to-end Entangled Segmentation and Classification Convolutional Neural Network for Periodontitis Stage Grading from Periapical Radiographic Images","date":"2021-09-27","arxiv_id":"2109.13120","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecg-beat-representation-and-delineation-by","title":"ECG Beat Representation and Delineation by means of Variable Projection","date":"2021-09-27","arxiv_id":"2109.13022","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-hybrid-convolutional-neural-network","title":"A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images","date":"2021-09-26","arxiv_id":"2109.12634","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-soft-labels-to-model-uncertainty-in","title":"Using Soft Labels to Model Uncertainty in Medical Image Segmentation","date":"2021-09-26","arxiv_id":"2109.12622","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-for-mitochondria","title":"Contrastive Learning for Mitochondria Segmentation","date":"2021-09-25","arxiv_id":"2109.12363","repositories_listed":0,"syntology":null},{"url":null,"slug":"recal-net-joint-region-channel-wise","title":"ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos","date":"2021-09-25","arxiv_id":"2109.12448","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-novel-customer-features-with","title":"Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation","date":"2021-09-24","arxiv_id":"2109.11871","repositories_listed":0,"syntology":null},{"url":null,"slug":"gsip-green-semantic-segmentation-of-large","title":"GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds","date":"2021-09-24","arxiv_id":"2109.11835","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-optical-neuroimaging-denoising-with","title":"Joint Optical Neuroimaging Denoising with Semantic Tasks","date":"2021-09-22","arxiv_id":"2109.10499","repositories_listed":0,"syntology":null},{"url":null,"slug":"nudgeseg-zero-shot-object-segmentation-by","title":"NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction","date":"2021-09-22","arxiv_id":"2109.13859","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-training-based-unsupervised-cross","title":"Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation","date":"2021-09-22","arxiv_id":"2109.10674","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-domain-shift-on-left-and-right","title":"The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images","date":"2021-09-22","arxiv_id":"2109.13230","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-difficulty-of-segmenting-words-with","title":"On the Difficulty of Segmenting Words with Attention","date":"2021-09-21","arxiv_id":"2109.10107","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-on-semantic-stereo-matching-semantic","title":"Survey on Semantic Stereo Matching / Semantic Depth Estimation","date":"2021-09-21","arxiv_id":"2109.10123","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-3d-ultrasound-segmentation-of","title":"Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning","date":"2021-09-20","arxiv_id":"2109.09283","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-in-recurrent-neural-networks-for","title":"Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality","date":"2021-09-20","arxiv_id":"2109.09425","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-ai-based-segmentation-of-apical-and","title":"Improved AI-based segmentation of apical and basal slices from clinical cine CMR","date":"2021-09-20","arxiv_id":"2109.09421","repositories_listed":0,"syntology":null},{"url":null,"slug":"rsi-net-two-stream-deep-neural-network","title":"RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Imagesbased Semantic Segmentation","date":"2021-09-19","arxiv_id":"2109.09148","repositories_listed":0,"syntology":null},{"url":null,"slug":"mass-segmentation-in-automated-3-d-breast","title":"Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net","date":"2021-09-17","arxiv_id":"2109.08330","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-multi-granularity-segmentation-for","title":"Modeling Multi-granularity Segmentation for Rare Words in Neural Machine Translation","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-unet-raw-image-processing-with","title":"Transformer-Unet: Raw Image Processing with Unet","date":"2021-09-17","arxiv_id":"2109.08417","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-risk-classification-of-colon","title":"Automated risk classification of colon biopsies based on semantic segmentation of histopathology images","date":"2021-09-16","arxiv_id":"2109.07892","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-padding-for-semantic","title":"Context-aware Padding for Semantic Segmentation","date":"2021-09-16","arxiv_id":"2109.07854","repositories_listed":0,"syntology":null},{"url":null,"slug":"navigation-oriented-scene-understanding-for","title":"Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images","date":"2021-09-15","arxiv_id":"2109.07245","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-search-in-operational","title":"Neural Architecture Search in operational context: a remote sensing case-study","date":"2021-09-15","arxiv_id":"2109.08028","repositories_listed":0,"syntology":null},{"url":"/paper/ucp-net-unstructured-contour-points-for","slug":"ucp-net-unstructured-contour-points-for","title":"UCP-Net: Unstructured Contour Points for Instance Segmentation","date":"2021-09-15","arxiv_id":"2109.07592","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-medical-image-1","title":"Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net","date":"2021-09-15","arxiv_id":"2109.07045","repositories_listed":0,"syntology":null},{"url":null,"slug":"3-dimensional-deep-learning-with-spatial","title":"3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI","date":"2021-09-14","arxiv_id":"2109.06540","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-dental-model-segmentation-with-graph","title":"3D Dental model segmentation with graph attentional convolution network","date":"2021-09-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-hippocampal-surface-generation-via","title":"Automatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization","date":"2021-09-14","arxiv_id":"2109.06817","repositories_listed":0,"syntology":null},{"url":null,"slug":"hardware-aware-real-time-myocardial","title":"Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography","date":"2021-09-14","arxiv_id":"2109.06909","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modality-domain-adaptation-for","title":"Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation","date":"2021-09-13","arxiv_id":"2109.06274","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-clinical-characteristics-for","title":"Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT","date":"2021-09-13","arxiv_id":"2109.05816","repositories_listed":0,"syntology":null},{"url":null,"slug":"popcorn-progressive-pseudo-labeling-with","title":"POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring","date":"2021-09-13","arxiv_id":"2109.06361","repositories_listed":0,"syntology":null},{"url":null,"slug":"border-seggcn-improving-semantic-segmentation","title":"Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline using Graph Convolutional Network","date":"2021-09-11","arxiv_id":"2109.05353","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeppyram-enabling-pyramid-view-and","title":"DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos","date":"2021-09-11","arxiv_id":"2109.05352","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fully-automated-segmentation-of-rat","title":"Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks","date":"2021-09-09","arxiv_id":"2109.04188","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-multi-view-ultrasonic-image","title":"Deep Learning for Multi-View Ultrasonic Image Fusion","date":"2021-09-08","arxiv_id":"2109.03616","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-building-segmentation-for-off-nadir","title":"Improving Building Segmentation for Off-Nadir Satellite Imagery","date":"2021-09-08","arxiv_id":"2109.03961","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssegep-small-segment-emphasized-performance","title":"SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation","date":"2021-09-08","arxiv_id":"2109.03435","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-tumor-segmentation-through","title":"Self-supervised Tumor Segmentation through Layer Decomposition","date":"2021-09-07","arxiv_id":"2109.03230","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-decoupled-uncertainty-model-for-mri","title":"A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation","date":"2021-09-06","arxiv_id":"2109.02413","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-segmentation-of-the-optic-nerve","title":"Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review","date":"2021-09-06","arxiv_id":"2109.02322","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-attention-layer-evolves-semantic","title":"Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms","date":"2021-09-06","arxiv_id":"2109.02711","repositories_listed":0,"syntology":null},{"url":null,"slug":"m-slae-net-multi-scale-multi-level-attention","title":"(M)SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation","date":"2021-09-05","arxiv_id":"2109.02084","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-facilitates-fully-automated","title":"Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging","date":"2021-09-04","arxiv_id":"2109.01880","repositories_listed":0,"syntology":null},{"url":null,"slug":"riwnet-a-moving-object-instance-segmentation","title":"RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions","date":"2021-09-04","arxiv_id":"2109.01820","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-semantic-segmentation-of-2","title":"Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke","date":"2021-09-04","arxiv_id":"2109.01887","repositories_listed":0,"syntology":null},{"url":null,"slug":"access-control-using-spatially-invariant","title":"Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models","date":"2021-09-03","arxiv_id":"2109.01332","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-on-vspw-dataset-through","title":"Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models","date":"2021-09-03","arxiv_id":"2109.01316","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-transfer-based-coronary-artery","title":"Style Transfer based Coronary Artery Segmentation in X-ray Angiogram","date":"2021-09-03","arxiv_id":"2109.01441","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-few-shot-segmentation-via","title":"Weakly Supervised Few-Shot Segmentation Via Meta-Learning","date":"2021-09-03","arxiv_id":"2109.01693","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-the-robustness-of-instance","title":"Benchmarking the Robustness of Instance Segmentation Models","date":"2021-09-02","arxiv_id":"2109.01123","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-learnable-flow-regularized","title":"An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation","date":"2021-09-01","arxiv_id":"2109.00622","repositories_listed":0,"syntology":null},{"url":null,"slug":"frustration-level-annotation-in-latvian","title":"Frustration Level Annotation in Latvian Tweets with Non-Lexical Means of Expression","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/imagetbad-a-3d-computed-tomography","slug":"imagetbad-a-3d-computed-tomography","title":"ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection","date":"2021-09-01","arxiv_id":"2109.00374","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-image-corpus-annotation-protocol","title":"Multilingual Image Corpus: Annotation Protocol","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"455bd77dcccb633017da62ffb33a2431af08b1d28794acda6d18c7309cfe99f5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}