{"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":"/method/u-net/papers/14","list_of":"/method/u-net","method":"U-Net","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":14,"pages_in_order":26,"rows_per_page":100,"rows":[1301,1400],"of":2588,"counts":{"archive_papers_tagged":2588,"with_a_code_link":1017,"where_syntology_ran_a_sample":156,"not_listed_spam_title":0,"listed":2588,"listed_where_code_ran":156,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":130,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":130,"listed_every_run_a_failure_of_syntologys_instrument":26,"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":"/method/u-net","prev":"/method/u-net/papers/13","next":"/method/u-net/papers/15","papers":[{"paper":null,"slug":"cyclegan-network-for-sheet-metal-welding","title":"Cyclegan Network for Sheet Metal Welding Drawing Translation","date":"2022-09-28","arxiv_id":"2209.14106","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-detection-of-enlarged","title":"Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI","date":"2022-09-27","arxiv_id":"2209.13727","n_code_links":0,"syntology":null},{"paper":"/paper/all-are-worth-words-a-vit-backbone-for-score","slug":"all-are-worth-words-a-vit-backbone-for-score","title":"All are Worth Words: A ViT Backbone for Diffusion Models","date":"2022-09-25","arxiv_id":"2209.12152","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["baofff/U-ViT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/rethinking-performance-gains-in-image","slug":"rethinking-performance-gains-in-image","title":"Rethinking Performance Gains in Image Dehazing Networks","date":"2022-09-23","arxiv_id":"2209.11448","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-ct-based-airway-segmentation-using-u-2-net","title":"A CT-Based Airway Segmentation Using U$^2$-net Trained by the Dice Loss Function","date":"2022-09-22","arxiv_id":"2209.10796","n_code_links":0,"syntology":null},{"paper":"/paper/calving-fronts-and-where-to-find-them-a","slug":"calving-fronts-and-where-to-find-them-a","title":"Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery","date":"2022-09-22","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-trio-method-for-retinal-vessel-segmentation","title":"A Trio-Method for Retinal Vessel Segmentation using Image Processing","date":"2022-09-19","arxiv_id":"2209.11230","n_code_links":0,"syntology":null},{"paper":"/paper/energy-efficient-automatic-streetlight","slug":"energy-efficient-automatic-streetlight","title":"CNN based Intelligent Streetlight Management Using Smart CCTV Camera and Semantic Segmentation","date":"2022-09-18","arxiv_id":"2209.08633","n_code_links":1,"syntology":null},{"paper":null,"slug":"through-a-fair-looking-glass-mitigating-bias","title":"Through a fair looking-glass: mitigating bias in image datasets","date":"2022-09-18","arxiv_id":"2209.08648","n_code_links":0,"syntology":null},{"paper":"/paper/automated-segmentation-and-recurrence-risk","slug":"automated-segmentation-and-recurrence-risk","title":"Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks","date":"2022-09-17","arxiv_id":"2209.08423","n_code_links":1,"syntology":null},{"paper":"/paper/improving-mitosis-detection-via-unet-based","slug":"improving-mitosis-detection-via-unet-based","title":"Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer","date":"2022-09-15","arxiv_id":"2209.09193","n_code_links":1,"syntology":null},{"paper":"/paper/nu-net-an-unpretentious-nested-u-net-for","slug":"nu-net-an-unpretentious-nested-u-net-for","title":"Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation","date":"2022-09-15","arxiv_id":"2209.07193","n_code_links":2,"syntology":null},{"paper":null,"slug":"the-development-of-spatial-attention-u-net","title":"The Development of Spatial Attention U-Net for The Recovery of Ionospheric Measurements and The Extraction of Ionospheric Parameters","date":"2022-09-15","arxiv_id":"2209.07581","n_code_links":0,"syntology":null},{"paper":"/paper/label-refinement-network-from-synthetic-error","slug":"label-refinement-network-from-synthetic-error","title":"Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation","date":"2022-09-14","arxiv_id":"2209.06353","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-segmentation-and","title":"Comparative analysis of segmentation and generative models for fingerprint retrieval task","date":"2022-09-13","arxiv_id":"2209.06172","n_code_links":0,"syntology":null},{"paper":"/paper/predicting-brain-multigraph-population-from-a","slug":"predicting-brain-multigraph-population-from-a","title":"Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification","date":"2022-09-13","arxiv_id":"2209.06005","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-fetal-fat-quantification-from-mri","title":"Automatic fetal fat quantification from MRI","date":"2022-09-08","arxiv_id":"2209.03748","n_code_links":0,"syntology":null},{"paper":"/paper/context-recovery-and-knowledge-retrieval-a","slug":"context-recovery-and-knowledge-retrieval-a","title":"Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection","date":"2022-09-07","arxiv_id":"2209.02899","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-evaluation-of-u-net-in-renal-structure","title":"An evaluation of U-Net in Renal Structure Segmentation","date":"2022-09-06","arxiv_id":"2209.02247","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-sensor-based-animal-behavior","title":"Improved Sensor-Based Animal Behavior Classification Performance through Conditional Generative Adversarial Network","date":"2022-09-06","arxiv_id":"2209.03758","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-outcome-of-the-2022-landslide4sense","title":"The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery","date":"2022-09-06","arxiv_id":"2209.02556","n_code_links":0,"syntology":null},{"paper":"/paper/ensemble-of-pre-trained-neural-networks-for","slug":"ensemble-of-pre-trained-neural-networks-for","title":"Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy Images","date":"2022-09-05","arxiv_id":"2209.01908","n_code_links":1,"syntology":null},{"paper":null,"slug":"autopet-challenge-combining-nn-unet-with-swin","title":"AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier","date":"2022-09-02","arxiv_id":"2209.01112","n_code_links":0,"syntology":null},{"paper":null,"slug":"physics-informed-mta-unet-prediction-of","title":"Physics-informed MTA-UNet: Prediction of Thermal Stress and Thermal Deformation of Satellites","date":"2022-09-01","arxiv_id":"2209.01009","n_code_links":0,"syntology":null},{"paper":"/paper/riesz-quincunx-unet-variational-auto-encoder","slug":"riesz-quincunx-unet-variational-auto-encoder","title":"Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising","date":"2022-08-25","arxiv_id":"2208.12810","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-modality-abdominal-multi-organ","title":"Multi-Modality Abdominal Multi-Organ Segmentation with Deep Supervised 3D Segmentation Model","date":"2022-08-24","arxiv_id":"2208.12041","n_code_links":0,"syntology":null},{"paper":"/paper/deep-3d-vessel-segmentation-based-on-cross","slug":"deep-3d-vessel-segmentation-based-on-cross","title":"Deep 3D Vessel Segmentation based on Cross Transformer Network","date":"2022-08-22","arxiv_id":"2208.10148","n_code_links":1,"syntology":null},{"paper":"/paper/exploiting-temporal-structures-of","slug":"exploiting-temporal-structures-of","title":"Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation","date":"2022-08-22","arxiv_id":"2208.10325","n_code_links":1,"syntology":null},{"paper":"/paper/pu-mfa-point-cloud-up-sampling-via-multi","slug":"pu-mfa-point-cloud-up-sampling-via-multi","title":"PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention","date":"2022-08-22","arxiv_id":"2208.10968","n_code_links":1,"syntology":null},{"paper":"/paper/parse-challenge-2022-pulmonary-arteries","slug":"parse-challenge-2022-pulmonary-arteries","title":"PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net","date":"2022-08-20","arxiv_id":"2208.09636","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-the-limits-of-synthetic-creation-of","slug":"exploring-the-limits-of-synthetic-creation-of","title":"Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation","date":"2022-08-19","arxiv_id":"2208.09512","n_code_links":1,"syntology":null},{"paper":null,"slug":"low-light-enhancement-method-based-on","title":"Low-light Enhancement Method Based on Attention Map Net","date":"2022-08-19","arxiv_id":"2208.09330","n_code_links":0,"syntology":null},{"paper":null,"slug":"uconv-conformer-high-reduction-of-input","title":"Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition","date":"2022-08-16","arxiv_id":"2208.07657","n_code_links":0,"syntology":null},{"paper":"/paper/voxels-intersecting-along-orthogonal-levels","slug":"voxels-intersecting-along-orthogonal-levels","title":"Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT","date":"2022-08-12","arxiv_id":"2208.06313","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-structure-segmentation-for-renal-cancer","title":"CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation","date":"2022-08-10","arxiv_id":"2208.05241","n_code_links":0,"syntology":null},{"paper":null,"slug":"preserving-the-beamforming-effect-for-spatial","title":"Preserving the beamforming effect for spatial cue-based pseudo-binaural dereverberation of a single source","date":"2022-08-10","arxiv_id":"2208.05184","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-ultrasound-image-segmentation-of","title":"Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture","date":"2022-08-09","arxiv_id":"2208.05050","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-centric-ai-approach-to-improve-optic","title":"Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images","date":"2022-08-08","arxiv_id":"2208.03868","n_code_links":0,"syntology":null},{"paper":"/paper/u-net-vs-transformer-is-u-net-outdated-in","slug":"u-net-vs-transformer-is-u-net-outdated-in","title":"U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?","date":"2022-08-07","arxiv_id":"2208.04939","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-automated-classification-and","title":"A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans","date":"2022-08-04","arxiv_id":"2208.02910","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-segmentation-of-the-placenta-in","slug":"automatic-segmentation-of-the-placenta-in","title":"Automatic Segmentation of the Placenta in BOLD MRI Time Series","date":"2022-08-04","arxiv_id":"2208.02895","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-tissue-segmentation-via-deep","title":"Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network","date":"2022-08-04","arxiv_id":"2208.02912","n_code_links":0,"syntology":null},{"paper":"/paper/spatio-temporal-u-net-for-cerebral-artery-and","slug":"spatio-temporal-u-net-for-cerebral-artery-and","title":"CAVE: Cerebral Artery-Vein Segmentation in Digital Subtraction Angiography","date":"2022-08-03","arxiv_id":"2208.02355","n_code_links":1,"syntology":null},{"paper":"/paper/resolution-enhancement-of-placenta","slug":"resolution-enhancement-of-placenta","title":"Resolution enhancement of placenta histological images using deep learning","date":"2022-07-30","arxiv_id":"2208.00163","n_code_links":1,"syntology":null},{"paper":null,"slug":"fcsn-global-context-aware-segmentation-by","title":"FCSN: Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images","date":"2022-07-29","arxiv_id":"2207.14477","n_code_links":0,"syntology":null},{"paper":null,"slug":"rha-net-an-encoder-decoder-network-with","title":"RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation","date":"2022-07-28","arxiv_id":"2207.14166","n_code_links":0,"syntology":null},{"paper":"/paper/training-a-universal-instance-segmentation","slug":"training-a-universal-instance-segmentation","title":"Training a universal instance segmentation network for live cell images of various cell types and imaging modalities","date":"2022-07-28","arxiv_id":"2207.14347","n_code_links":1,"syntology":null},{"paper":"/paper/generator-knows-what-discriminator-should","slug":"generator-knows-what-discriminator-should","title":"Generator Knows What Discriminator Should Learn in Unconditional GANs","date":"2022-07-27","arxiv_id":"2207.13320","n_code_links":1,"syntology":null},{"paper":"/paper/transnorm-transformer-provides-a-strong","slug":"transnorm-transformer-provides-a-strong","title":"TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model","date":"2022-07-27","arxiv_id":"2207.13415","n_code_links":1,"syntology":null},{"paper":"/paper/generalized-probabilistic-u-net-for-medical","slug":"generalized-probabilistic-u-net-for-medical","title":"Generalized Probabilistic U-Net for medical image segementation","date":"2022-07-26","arxiv_id":"2207.12872","n_code_links":1,"syntology":null},{"paper":"/paper/tinycd-a-not-so-deep-learning-model-for","slug":"tinycd-a-not-so-deep-learning-model-for","title":"TINYCD: A (Not So) Deep Learning Model For Change Detection","date":"2022-07-26","arxiv_id":"2207.13159","n_code_links":2,"syntology":null},{"paper":"/paper/octave-2d-en-face-optical-coherence","slug":"octave-2d-en-face-optical-coherence","title":"OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation","date":"2022-07-25","arxiv_id":"2207.12238","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparative-validation-of-ai-and-non-ai","title":"Comparative Validation of AI and non-AI Methods in MRI Volumetry to Diagnose Parkinsonian Syndromes","date":"2022-07-23","arxiv_id":"2207.11534","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-landmark-based-stent-tracking-in-x-ray","title":"Robust Landmark-based Stent Tracking in X-ray Fluoroscopy","date":"2022-07-20","arxiv_id":"2207.09933","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-automatic-prostate-zones","title":"Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures","date":"2022-07-19","arxiv_id":"2207.09483","n_code_links":0,"syntology":null},{"paper":null,"slug":"gafx-a-general-audio-feature-extractor","title":"GAFX: A General Audio Feature eXtractor","date":"2022-07-19","arxiv_id":"2207.09145","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-kernel-attention-for-3d-medical-image","title":"Large-Kernel Attention for 3D Medical Image Segmentation","date":"2022-07-19","arxiv_id":"2207.11225","n_code_links":0,"syntology":null},{"paper":"/paper/vologan-adversarial-domain-adaptation-for","slug":"vologan-adversarial-domain-adaptation-for","title":"VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data","date":"2022-07-19","arxiv_id":"2207.09204","n_code_links":1,"syntology":null},{"paper":"/paper/fully-trainable-gaussian-derivative","slug":"fully-trainable-gaussian-derivative","title":"Fully trainable Gaussian derivative convolutional layer","date":"2022-07-18","arxiv_id":"2207.08424","n_code_links":1,"syntology":null},{"paper":null,"slug":"mlp-gan-for-brain-vessel-image-segmentation","title":"MLP-GAN for Brain Vessel Image Segmentation","date":"2022-07-17","arxiv_id":"2207.08265","n_code_links":0,"syntology":null},{"paper":null,"slug":"monitoring-vegetation-from-space-at-extremely","title":"Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net","date":"2022-07-16","arxiv_id":"2207.08022","n_code_links":0,"syntology":null},{"paper":null,"slug":"rain-rate-estimation-with-sar-using-nexrad","title":"Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks","date":"2022-07-15","arxiv_id":"2207.07333","n_code_links":0,"syntology":null},{"paper":null,"slug":"imaging-through-the-atmosphere-using","title":"Imaging through the Atmosphere using Turbulence Mitigation Transformer","date":"2022-07-13","arxiv_id":"2207.06465","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-prediction-of-monocular-depth-and","title":"Joint Prediction of Monocular Depth and Structure using Planar and Parallax Geometry","date":"2022-07-13","arxiv_id":"2207.06351","n_code_links":0,"syntology":null},{"paper":null,"slug":"left-ventricle-contouring-of-apical-three","title":"Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography","date":"2022-07-13","arxiv_id":"2207.06330","n_code_links":0,"syntology":null},{"paper":null,"slug":"yolo2u-net-detection-guided-3d-instance","title":"YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopy","date":"2022-07-13","arxiv_id":"2207.06215","n_code_links":0,"syntology":null},{"paper":"/paper/m-fuse-multi-frame-fusion-for-scene-flow","slug":"m-fuse-multi-frame-fusion-for-scene-flow","title":"M-FUSE: Multi-frame Fusion for Scene Flow Estimation","date":"2022-07-12","arxiv_id":"2207.05704","n_code_links":1,"syntology":null},{"paper":null,"slug":"vertxnet-automatic-segmentation-and","title":"VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images","date":"2022-07-12","arxiv_id":"2207.05476","n_code_links":0,"syntology":null},{"paper":null,"slug":"rank-enhanced-low-dimensional-convolution-set","title":"Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising","date":"2022-07-09","arxiv_id":"2207.04266","n_code_links":0,"syntology":null},{"paper":"/paper/tfcns-a-cnn-transformer-hybrid-network-for","slug":"tfcns-a-cnn-transformer-hybrid-network-for","title":"TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation","date":"2022-07-07","arxiv_id":"2207.03450","n_code_links":1,"syntology":null},{"paper":"/paper/is-the-u-net-directional-relationship-aware","slug":"is-the-u-net-directional-relationship-aware","title":"Is the U-Net Directional-Relationship Aware?","date":"2022-07-06","arxiv_id":"2207.02574","n_code_links":1,"syntology":null},{"paper":null,"slug":"perfusion-imaging-in-deep-prostate-cancer","title":"Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?","date":"2022-07-06","arxiv_id":"2207.02854","n_code_links":0,"syntology":null},{"paper":"/paper/swin-deformable-attention-u-net-transformer","slug":"swin-deformable-attention-u-net-transformer","title":"Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI","date":"2022-07-05","arxiv_id":"2207.02390","n_code_links":1,"syntology":null},{"paper":null,"slug":"solving-learn-to-race-autonomous-racing","title":"Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space","date":"2022-07-04","arxiv_id":"2207.01275","n_code_links":0,"syntology":null},{"paper":null,"slug":"vehicle-trajectory-prediction-on-highways","title":"Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning","date":"2022-07-04","arxiv_id":"2207.01407","n_code_links":0,"syntology":null},{"paper":"/paper/generating-gender-ambiguous-voices-for","slug":"generating-gender-ambiguous-voices-for","title":"Generating gender-ambiguous voices for privacy-preserving speech recognition","date":"2022-07-03","arxiv_id":"2207.01052","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-segment-prostate-cancer-by","title":"Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI","date":"2022-07-01","arxiv_id":"2207.05056","n_code_links":0,"syntology":null},{"paper":null,"slug":"implicit-u-net-for-volumetric-medical-image","title":"Implicit U-Net for volumetric medical image segmentation","date":"2022-06-30","arxiv_id":"2206.15217","n_code_links":0,"syntology":null},{"paper":"/paper/semantic-image-synthesis-via-diffusion-models","slug":"semantic-image-synthesis-via-diffusion-models","title":"Semantic Image Synthesis via Diffusion Models","date":"2022-06-30","arxiv_id":"2207.00050","n_code_links":4,"syntology":{"ran":28,"of":32,"n_ran_checked":23,"n_instrument":5,"unverified":4,"pointer_only":10,"phrase":"28 ran (of which 0 constructed an object rather than computing a result; 23 with no instrument failure: 2 honoured, 1 violated, 20 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","official":{"repos":["weilunwang/semantic-diffusion-model"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"cut-inner-layers-a-structured-pruning","title":"Cut Inner Layers: A Structured Pruning Strategy for Efficient U-Net GANs","date":"2022-06-29","arxiv_id":"2206.14658","n_code_links":0,"syntology":null},{"paper":null,"slug":"gan-based-super-resolution-and-segmentation","title":"GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans","date":"2022-06-28","arxiv_id":"2206.13740","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-disease-classification-performance","title":"Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators","date":"2022-06-28","arxiv_id":"2207.00157","n_code_links":0,"syntology":null},{"paper":"/paper/extended-u-net-for-speaker-verification-in","slug":"extended-u-net-for-speaker-verification-in","title":"Extended U-Net for Speaker Verification in Noisy Environments","date":"2022-06-27","arxiv_id":"2206.13044","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-extraction-of-coronary-arteries","title":"Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms","date":"2022-06-24","arxiv_id":"2206.12300","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrastive-learning-of-features-between","title":"Contrastive Learning of Features between Images and LiDAR","date":"2022-06-24","arxiv_id":"2206.12071","n_code_links":0,"syntology":null},{"paper":"/paper/icos-protein-expression-segmentation-can","slug":"icos-protein-expression-segmentation-can","title":"ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?","date":"2022-06-23","arxiv_id":"2206.11520","n_code_links":1,"syntology":null},{"paper":null,"slug":"cnn-based-fully-automatic-wrist-cartilage","title":"CNN-based fully automatic wrist cartilage volume quantification in MR Image","date":"2022-06-22","arxiv_id":"2206.11127","n_code_links":0,"syntology":null},{"paper":null,"slug":"floor-map-reconstruction-through-radio","title":"Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface","date":"2022-06-21","arxiv_id":"2206.10750","n_code_links":0,"syntology":null},{"paper":"/paper/using-the-polar-transform-for-efficient-deep","slug":"using-the-polar-transform-for-efficient-deep","title":"Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images","date":"2022-06-21","arxiv_id":"2206.10294","n_code_links":1,"syntology":null},{"paper":null,"slug":"free-form-lesion-synthesis-using-a-partial","title":"Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation","date":"2022-06-18","arxiv_id":"2206.09065","n_code_links":0,"syntology":null},{"paper":"/paper/multistream-gaze-estimation-with-anatomical","slug":"multistream-gaze-estimation-with-anatomical","title":"Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning","date":"2022-06-18","arxiv_id":"2206.09256","n_code_links":1,"syntology":null},{"paper":null,"slug":"du-net-based-unsupervised-contrastive","title":"DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images","date":"2022-06-17","arxiv_id":"2206.08791","n_code_links":0,"syntology":null},{"paper":"/paper/enhanced-bi-directional-motion-estimation-for","slug":"enhanced-bi-directional-motion-estimation-for","title":"Enhanced Bi-directional Motion Estimation for Video Frame Interpolation","date":"2022-06-17","arxiv_id":"2206.08572","n_code_links":1,"syntology":null},{"paper":"/paper/nucleus-segmentation-and-analysis-in-breast","slug":"nucleus-segmentation-and-analysis-in-breast","title":"Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Framework","date":"2022-06-16","arxiv_id":"2206.08182","n_code_links":2,"syntology":null},{"paper":null,"slug":"u-pet-mri-based-dementia-detection-with-joint","title":"U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images","date":"2022-06-16","arxiv_id":"2206.08078","n_code_links":0,"syntology":null},{"paper":"/paper/asymmetric-dual-decoder-u-net-for-joint-rain","slug":"asymmetric-dual-decoder-u-net-for-joint-rain","title":"Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal","date":"2022-06-14","arxiv_id":"2206.06803","n_code_links":1,"syntology":null},{"paper":"/paper/federated-multi-organ-segmentation-with","slug":"federated-multi-organ-segmentation-with","title":"Federated Multi-organ Segmentation with Inconsistent Labels","date":"2022-06-14","arxiv_id":"2206.07156","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-ensemble-learning-for-segmenting","title":"Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs","date":"2022-06-13","arxiv_id":"2206.06065","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-time-series-denoising-with-learnable","title":"Robust Time Series Denoising with Learnable Wavelet Packet Transform","date":"2022-06-13","arxiv_id":"2206.06126","n_code_links":0,"syntology":null},{"paper":null,"slug":"mammodl-mammographic-breast-density","title":"MammoFL: Mammographic Breast Density Estimation using Federated Learning","date":"2022-06-11","arxiv_id":"2206.05575","n_code_links":0,"syntology":null}],"record_sha256":"9358e63c2ddc07ecf3bb27ccda3bd3d86123ec29be361ae8c12ceb10e78e638b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}