{"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/34","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":34,"pages_in_order":51,"rows_per_page":100,"rows":[3301,3400],"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/33","next":"/task/image-segmentation/papers/35","papers":[{"url":null,"slug":"variational-multichannel-multiclass","title":"Variational multichannel multiclass segmentation using unsupervised lifting with CNNs","date":"2023-02-04","arxiv_id":"2302.02214","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-3d-medical-image","title":"Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity","date":"2023-02-04","arxiv_id":"2302.02125","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-segment-towards-a-single-unified","title":"Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans","date":"2023-02-01","arxiv_id":"2302.00162","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-u-net-faster-greater-and-noiseless","title":"Continuous U-Net: Faster, Greater and Noiseless","date":"2023-02-01","arxiv_id":"2302.00626","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-superpixel-based-image-segmentation","title":"Fuzzy Superpixel-based Image Segmentation","date":"2023-02-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-mitral-valve-hinge-point","title":"Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation","date":"2023-01-20","arxiv_id":"2301.08782","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-dimensional-reconstruction-and","title":"Three-dimensional reconstruction and characterization of bladder deformations","date":"2023-01-18","arxiv_id":"2301.07385","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-as-a-gateway-to","title":"Artificial intelligence as a gateway to scientific discovery: Uncovering features in retinal fundus images","date":"2023-01-17","arxiv_id":"2301.06675","repositories_listed":0,"syntology":null},{"url":null,"slug":"segviz-a-federated-learning-framework-for","title":"SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations","date":"2023-01-17","arxiv_id":"2301.07074","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-aligned-cross-modal-representations-1","title":"Linguistic Query-Guided Mask Generation for Referring Image Segmentation","date":"2023-01-16","arxiv_id":"2301.06429","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-train-adaptive-u-net-for-image","title":"Post-Train Adaptive U-Net for Image Segmentation","date":"2023-01-16","arxiv_id":"2301.06358","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-review-of-modern-object","title":"A Comprehensive Review of Modern Object Segmentation Approaches","date":"2023-01-13","arxiv_id":"2301.07499","repositories_listed":0,"syntology":null},{"url":null,"slug":"zscribbleseg-zen-and-the-art-of-scribble","title":"ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation","date":"2023-01-12","arxiv_id":"2301.04882","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-quantum-fourier-transform-inspired","title":"Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised Image Segmentation","date":"2023-01-11","arxiv_id":"2301.04705","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-the-most-transferable-tasks-for-brain","title":"Finding the Most Transferable Tasks for Brain Image Segmentation","date":"2023-01-03","arxiv_id":"2301.00934","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-quest-of-ground-truth-learning-confident","title":"Learning Confident Classifiers in the Presence of Label Noise","date":"2023-01-02","arxiv_id":"2301.00524","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-segment-towards-a-single-unified-1","title":"Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"high-quality-entity-segmentation","title":"High Quality Entity Segmentation","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-exploit-the-sequence-specific","title":"Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-spectral-polarization-propagation","title":"Multi-view Spectral Polarization Propagation for Video Glass Segmentation","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segloc-learning-segmentation-based","title":"SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/segment-every-reference-object-in-spatial-and","slug":"segment-every-reference-object-in-spatial-and","title":"Segment Every Reference Object in Spatial and Temporal Spaces","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-referring-image","title":"Weakly Supervised Referring Image Segmentation with Intra-Chunk and Inter-Chunk Consistency","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-malfunctioning-modules-in","title":"Detection of Malfunctioning Modules in Photovoltaic Power Plants using Unsupervised Feature Clustering Segmentation Algorithm","date":"2022-12-30","arxiv_id":"2212.14653","repositories_listed":0,"syntology":null},{"url":null,"slug":"informing-selection-of-performance-metrics","title":"Informing selection of performance metrics for medical image segmentation evaluation using configurable synthetic errors","date":"2022-12-30","arxiv_id":"2212.14828","repositories_listed":0,"syntology":null},{"url":null,"slug":"myi-net-fully-automatic-detection-and","title":"MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images","date":"2022-12-28","arxiv_id":"2212.13715","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixel-relationships-based-regularizer-for","title":"Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation","date":"2022-12-28","arxiv_id":"2212.13731","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-aware-contrastive-alignment-for","title":"Position-Aware Contrastive Alignment for Referring Image Segmentation","date":"2022-12-27","arxiv_id":"2212.13419","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-most-general-manner-to-injectively-align","title":"The most general manner to injectively align true and predicted segments","date":"2022-12-27","arxiv_id":"2212.13445","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-domain-adaptation-for-4","title":"Semi-Supervised Domain Adaptation for Semantic Segmentation of Roads from Satellite Images","date":"2022-12-26","arxiv_id":"2212.13079","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-monocular-depth-estimation-1","title":"Lightweight Monocular Depth Estimation","date":"2022-12-21","arxiv_id":"2212.11363","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-based-unsupervised","title":"Image Segmentation-based Unsupervised Multiple Objects Discovery","date":"2022-12-20","arxiv_id":"2212.10124","repositories_listed":0,"syntology":null},{"url":null,"slug":"dgnet-distribution-guided-efficient-learning","title":"DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation","date":"2022-12-19","arxiv_id":"2301.01202","repositories_listed":0,"syntology":null},{"url":null,"slug":"annotation-by-clicks-a-point-supervised","title":"Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation","date":"2022-12-17","arxiv_id":"2212.08774","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-siamese-network-for-robust-change","title":"Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing","date":"2022-12-16","arxiv_id":"2212.08583","repositories_listed":0,"syntology":null},{"url":"/paper/multi-task-fusion-for-efficient-panoptic-part","slug":"multi-task-fusion-for-efficient-panoptic-part","title":"Multi-task Fusion for Efficient Panoptic-Part Segmentation","date":"2022-12-15","arxiv_id":"2212.07671","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-bayesian-uncertainty-estimation-for","title":"Efficient Bayesian Uncertainty Estimation for nnU-Net","date":"2022-12-12","arxiv_id":"2212.06278","repositories_listed":0,"syntology":null},{"url":null,"slug":"unet-based-pipeline-for-lung-segmentation","title":"UNet Based Pipeline for Lung Segmentation from Chest X-Ray Images","date":"2022-12-09","arxiv_id":"2212.04617","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-medical-image-segmentation-with","title":"Few-shot Medical Image Segmentation with Cycle-resemblance Attention","date":"2022-12-07","arxiv_id":"2212.03967","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-particle-motions-in-stabilized","title":"Quantification of geogrid lateral restraint using transparent sand and deep learning-based image segmentation","date":"2022-12-06","arxiv_id":"2212.02939","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-image-segmentation-for-high","title":"Supervised Image Segmentation for High Dynamic Range Imaging","date":"2022-12-06","arxiv_id":"2212.03002","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimum-class-confusion-based-transfer-for","title":"Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions","date":"2022-12-05","arxiv_id":"2212.02130","repositories_listed":0,"syntology":null},{"url":null,"slug":"coupalign-coupling-word-pixel-with-sentence","title":"CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation","date":"2022-12-04","arxiv_id":"2212.01769","repositories_listed":0,"syntology":null},{"url":"/paper/ebhi-seg-a-novel-enteroscope-biopsy","slug":"ebhi-seg-a-novel-enteroscope-biopsy","title":"EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks","date":"2022-12-01","arxiv_id":"2212.00532","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-semantic-knowledge-from-gans-with","title":"Extracting Semantic Knowledge from GANs with Unsupervised Learning","date":"2022-11-30","arxiv_id":"2211.16710","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuro-symbolic-spatio-temporal-reasoning","title":"Neuro-Symbolic Spatio-Temporal Reasoning","date":"2022-11-28","arxiv_id":"2211.15566","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-domain-gap-in-frequency-and-spatial","title":"Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation","date":"2022-11-28","arxiv_id":"2211.15235","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-training-procedure","title":"Deep Learning Training Procedure Augmentations","date":"2022-11-25","arxiv_id":"2211.14395","repositories_listed":0,"syntology":null},{"url":null,"slug":"doubleu-netplus-a-novel-attention-and-context","title":"DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Images","date":"2022-11-25","arxiv_id":"2211.14235","repositories_listed":0,"syntology":null},{"url":"/paper/ss-cxr-multitask-representation-learning","slug":"ss-cxr-multitask-representation-learning","title":"SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model","date":"2022-11-23","arxiv_id":"2211.12944","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-for-semantic-image","title":"Synthetic Data for Semantic Image Segmentation of Imagery of Unmanned Spacecraft","date":"2022-11-22","arxiv_id":"2211.11941","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automated-polyp-segmentation-using","title":"Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers","date":"2022-11-21","arxiv_id":"2211.11847","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-medical-2","title":"Convolutional neural networks for medical image segmentation","date":"2022-11-17","arxiv_id":"2211.09562","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-transformer-with-hybrid","title":"Parameter-Efficient Transformer with Hybrid Axial-Attention for Medical Image Segmentation","date":"2022-11-17","arxiv_id":"2211.09533","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-and-self-supervised","title":"Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation","date":"2022-11-16","arxiv_id":"2211.08840","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-multi-parametric-magnetic","title":"Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation","date":"2022-11-16","arxiv_id":"2211.08783","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-point-in-the-right-direction-vector","title":"A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning","date":"2022-11-15","arxiv_id":"2211.08533","repositories_listed":0,"syntology":null},{"url":null,"slug":"convformer-combining-cnn-and-transformer-for","title":"ConvFormer: Combining CNN and Transformer for Medical Image Segmentation","date":"2022-11-15","arxiv_id":"2211.08564","repositories_listed":0,"syntology":null},{"url":null,"slug":"digest-deeply-supervised-knowledge-transfer","title":"DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans","date":"2022-11-15","arxiv_id":"2211.07993","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedback-chain-network-for-hippocampus","title":"Feedback Chain Network For Hippocampus Segmentation","date":"2022-11-15","arxiv_id":"2211.07891","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-feature-clustering-improves","title":"Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation","date":"2022-11-15","arxiv_id":"2211.08557","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-of-transformers-for","title":"A Comprehensive Survey of Transformers for Computer Vision","date":"2022-11-11","arxiv_id":"2211.06004","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-uncertainty-based-out-of","title":"Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation","date":"2022-11-10","arxiv_id":"2211.05421","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-incremental-cardiac-image-segmentation","title":"Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening","date":"2022-11-09","arxiv_id":"2211.04862","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-error-detection-in-integrated","title":"Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach","date":"2022-11-08","arxiv_id":"2211.03927","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-single-image-depth-estimation-on","title":"Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report","date":"2022-11-07","arxiv_id":"2211.04470","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-boundary-detection-for","title":"High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss","date":"2022-11-04","arxiv_id":"2211.02419","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-the-effectiveness-of-a-generative","title":"Analysing the effectiveness of a generative model for semi-supervised medical image segmentation","date":"2022-11-03","arxiv_id":"2211.01886","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-augmentation-improves-medical-image","title":"Style Augmentation improves Medical Image Segmentation","date":"2022-11-02","arxiv_id":"2211.01125","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-model-adaptation-for-source-free","title":"Unsupervised Model Adaptation for Source-free Segmentation of Medical Images","date":"2022-11-02","arxiv_id":"2211.00807","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-global-wildfire-forecasting","title":"Deep Learning for Global Wildfire Forecasting","date":"2022-11-01","arxiv_id":"2211.00534","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-structure-wise-uncertainty-for-3d","title":"Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation","date":"2022-11-01","arxiv_id":"2211.00303","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-generalization-the-impact-of","title":"Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation","date":"2022-10-31","arxiv_id":"2210.17398","repositories_listed":0,"syntology":null},{"url":null,"slug":"saliency-can-be-all-you-need-in-contrastive","title":"Saliency Can Be All You Need In Contrastive Self-Supervised Learning","date":"2022-10-30","arxiv_id":"2210.16776","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-randomized-smoothing-for-collective-1","title":"Localized Randomized Smoothing for Collective Robustness Certification","date":"2022-10-28","arxiv_id":"2210.16140","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-diffusion-models-via-pre","title":"Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation","date":"2022-10-27","arxiv_id":"2210.17408","repositories_listed":0,"syntology":null},{"url":null,"slug":"unet-2022-exploring-dynamics-in-non","title":"UNet-2022: Exploring Dynamics in Non-isomorphic Architecture","date":"2022-10-27","arxiv_id":"2210.15566","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-precise-are-performance-estimates-for","title":"How precise are performance estimates for typical medical image segmentation tasks?","date":"2022-10-26","arxiv_id":"2210.14677","repositories_listed":0,"syntology":null},{"url":null,"slug":"connectedunets-mass-segmentation-from-whole","title":"ConnectedUNets++: Mass Segmentation from Whole Mammographic Images","date":"2022-10-25","arxiv_id":"2210.13668","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-batch-and-patch-size-training-for","title":"Large Batch and Patch Size Training for Medical Image Segmentation","date":"2022-10-24","arxiv_id":"2210.13364","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-image-segmentation-with-deep-1","title":"Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping","date":"2022-10-24","arxiv_id":"2210.13296","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversity-promoting-ensemble-for-medical","title":"Diversity-Promoting Ensemble for Medical Image Segmentation","date":"2022-10-22","arxiv_id":"2210.12388","repositories_listed":0,"syntology":null},{"url":null,"slug":"ms-dc-unext-an-mlp-based-multi-scale-feature","title":"MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images","date":"2022-10-22","arxiv_id":"2210.12361","repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-image-semantic-segmentation","slug":"unsupervised-image-semantic-segmentation","title":"Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks","date":"2022-10-21","arxiv_id":"2210.11810","repositories_listed":0,"syntology":null},{"url":null,"slug":"rais-robust-and-accurate-interactive","title":"RAIS: Robust and Accurate Interactive Segmentation via Continual Learning","date":"2022-10-20","arxiv_id":"2210.10984","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-semantic-relation-generation","title":"Image Semantic Relation Generation","date":"2022-10-19","arxiv_id":"2210.11253","repositories_listed":0,"syntology":null},{"url":null,"slug":"otsu-based-differential-evolution-method-for","title":"Otsu based Differential Evolution Method for Image Segmentation","date":"2022-10-18","arxiv_id":"2210.10005","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-multi-modal-semantic-fusion-on-1","title":"Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation","date":"2022-10-18","arxiv_id":"2210.09739","repositories_listed":0,"syntology":null},{"url":null,"slug":"cutting-splicing-data-augmentation-a-novel","title":"Cutting-Splicing data augmentation: A novel technology for medical image segmentation","date":"2022-10-17","arxiv_id":"2210.09099","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-feature-distillation-network","title":"Heterogeneous Feature Distillation Network for SAR Image Semantic Segmentation","date":"2022-10-17","arxiv_id":"2210.08988","repositories_listed":0,"syntology":null},{"url":null,"slug":"mkis-net-a-light-weight-multi-kernel-network","title":"MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation","date":"2022-10-15","arxiv_id":"2210.08168","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-limited-tissue-segmentation-using","title":"Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning","date":"2022-10-14","arxiv_id":"2210.07936","repositories_listed":0,"syntology":null},{"url":null,"slug":"monodvps-a-self-supervised-monocular-depth","title":"MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation","date":"2022-10-14","arxiv_id":"2210.07577","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-to-real-composite-semantic","title":"Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing","date":"2022-10-14","arxiv_id":"2210.07466","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-dense-nuclei-detection-and","title":"Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images","date":"2022-10-14","arxiv_id":"2210.07862","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-clustering-network-for-unsupervised","title":"ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation","date":"2022-10-12","arxiv_id":"2210.05944","repositories_listed":0,"syntology":null},{"url":null,"slug":"digitization-of-raster-logs-a-deep-learning","title":"Digitization of Raster Logs: A Deep Learning Approach","date":"2022-10-11","arxiv_id":"2210.05597","repositories_listed":0,"syntology":null},{"url":null,"slug":"dpanet-dual-pooling-attention-network-for","title":"DPANET:Dual Pooling Attention Network for Semantic Segmentation","date":"2022-10-11","arxiv_id":"2210.05437","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-transformers-for-full-context-and-high","title":"Memory transformers for full context and high-resolution 3D Medical Segmentation","date":"2022-10-11","arxiv_id":"2210.05313","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fast-and-accurate-approach-to-detection","title":"The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning","date":"2022-10-11","arxiv_id":"2210.05167","repositories_listed":0,"syntology":null}],"record_sha256":"f1c193f267ac422a386af9589d8fd51814bec8b008421d2526b173b57b4d0f62","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}