{"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/medical-image-analysis/papers/7","list_of":"/task/medical-image-analysis","task":"Medical Image Analysis","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":7,"pages_in_order":14,"rows_per_page":100,"rows":[601,700],"of":1360,"counts":{"archive_papers_tagged":1360,"with_a_code_link":532,"where_syntology_ran_a_sample":87,"not_listed_spam_title":0,"listed":1360,"listed_where_code_ran":87,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":74,"every_run_a_failure_of_syntologys_instrument":13,"listed_with_a_run_with_no_instrument_failure":74,"listed_every_run_a_failure_of_syntologys_instrument":13,"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/medical-image-analysis","prev":"/task/medical-image-analysis/papers/6","next":"/task/medical-image-analysis/papers/8","papers":[{"url":null,"slug":"enhancing-diagnostic-in-3d-covid-19-pneumonia","title":"Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification","date":"2025-01-18","arxiv_id":"2501.10770","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-few-shot-medical-image-analysis-via","title":"Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning","date":"2025-01-16","arxiv_id":"2501.09294","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-distance-map-regression-network-with","title":"Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation","date":"2025-01-15","arxiv_id":"2501.09116","repositories_listed":0,"syntology":null},{"url":null,"slug":"medgrad-e-clip-enhancing-trust-and","title":"MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis","date":"2025-01-12","arxiv_id":"2501.06887","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-steerable-deep-network-for-model-free","title":"A Steerable Deep Network for Model-Free Diffusion MRI Registration","date":"2025-01-08","arxiv_id":"2501.04794","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-adaptive-vision-language-model-for-3d","title":"Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein","date":"2025-01-07","arxiv_id":"2501.03722","repositories_listed":0,"syntology":null},{"url":null,"slug":"semise-semi-supervised-learning-for-severity","title":"Semise: Semi-supervised learning for severity representation in medical image","date":"2025-01-07","arxiv_id":"2501.03848","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-one-shot-federated-ensemble","title":"Multi-Modal One-Shot Federated Ensemble Learning for Medical Data with Vision Large Language Model","date":"2025-01-06","arxiv_id":"2501.03292","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantic-knowledge-complementarity-based","title":"A Semantic Knowledge Complementarity based Decoupling Framework for Semi-supervised Class-imbalanced Medical Image Segmentation","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-vision-pre-training-for-medical","title":"Multi-modal Vision Pre-training for Medical Image Analysis","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-deep-learning-and-model-checking","title":"A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation","date":"2024-12-31","arxiv_id":"2501.01991","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformable-convolution-for-topologically","title":"Conformable Convolution for Topologically Aware Learning of Complex Anatomical Structures","date":"2024-12-29","arxiv_id":"2412.20608","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-in-image-classification","title":"Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data","date":"2024-12-29","arxiv_id":"2412.20345","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-transfer-learning-for-medical-image","title":"Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study","date":"2024-12-28","arxiv_id":"2412.20235","repositories_listed":0,"syntology":null},{"url":null,"slug":"neighbor-does-matter-density-aware","title":"Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation","date":"2024-12-27","arxiv_id":"2412.19871","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedvck-non-iid-robust-and-communication","title":"FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis","date":"2024-12-24","arxiv_id":"2412.18557","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-based-approaches-in-medical-image","title":"Diffusion-Based Approaches in Medical Image Generation and Analysis","date":"2024-12-22","arxiv_id":"2412.16860","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-guided-medical-image-segmentation","title":"Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments","date":"2024-12-18","arxiv_id":"2412.13533","repositories_listed":0,"syntology":null},{"url":null,"slug":"embeddings-are-all-you-need-achieving-high","title":"Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis","date":"2024-12-12","arxiv_id":"2412.09445","repositories_listed":0,"syntology":null},{"url":null,"slug":"euclid-supercharging-multimodal-llms-with","title":"Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions","date":"2024-12-11","arxiv_id":"2412.08737","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-prediction-of-stroke-treatment","title":"Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives","date":"2024-12-06","arxiv_id":"2412.04812","repositories_listed":0,"syntology":null},{"url":null,"slug":"backdooring-outlier-detection-methods-a-novel","title":"Backdooring Outlier Detection Methods: A Novel Attack Approach","date":"2024-12-06","arxiv_id":"2412.05010","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-diagnosis-leveraging-causal-modeling-to","title":"Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias","date":"2024-12-06","arxiv_id":"2412.04739","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-in-medical-image-analysis","title":"Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications","date":"2024-12-05","arxiv_id":"2412.03924","repositories_listed":0,"syntology":null},{"url":null,"slug":"insight-explainable-weakly-supervised-medical","title":"INSIGHT: Explainable Weakly-Supervised Medical Image Analysis","date":"2024-12-02","arxiv_id":"2412.02012","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-feature-enhancement-in-multi-task","title":"Multi-scale Feature Enhancement in Multi-task Learning for Medical Image Analysis","date":"2024-11-30","arxiv_id":"2412.00351","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-and-improving-the-effectiveness-of","title":"Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis","date":"2024-11-27","arxiv_id":"2411.18602","repositories_listed":0,"syntology":null},{"url":null,"slug":"tafm-net-a-novel-approach-to-skin-lesion","title":"TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation","date":"2024-11-26","arxiv_id":"2411.17556","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-domain-adaptation-with-dual-branch","title":"Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction","date":"2024-11-21","arxiv_id":"2411.14001","repositories_listed":0,"syntology":null},{"url":null,"slug":"med-2e3-a-2d-enhanced-3d-medical-multimodal","title":"Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model","date":"2024-11-19","arxiv_id":"2411.12783","repositories_listed":0,"syntology":null},{"url":null,"slug":"lung-disease-detection-with-vision","title":"Lung Disease Detection with Vision Transformers: A Comparative Study of Machine Learning Methods","date":"2024-11-18","arxiv_id":"2411.11376","repositories_listed":0,"syntology":null},{"url":null,"slug":"retinal-vessel-segmentation-via-neuron","title":"Retinal Vessel Segmentation via Neuron Programming","date":"2024-11-17","arxiv_id":"2411.11110","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-vision-autoregressive-model","title":"A Survey on Vision Autoregressive Model","date":"2024-11-13","arxiv_id":"2411.08666","repositories_listed":0,"syntology":null},{"url":null,"slug":"vit-enhanced-privacy-preserving-secure","title":"ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification","date":"2024-11-08","arxiv_id":"2411.05901","repositories_listed":0,"syntology":null},{"url":null,"slug":"navigating-distribution-shifts-in-medical","title":"Navigating Distribution Shifts in Medical Image Analysis: A Survey","date":"2024-11-05","arxiv_id":"2411.05824","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-mae-pre-training-for-3d-medical","title":"Revisiting MAE pre-training for 3D medical image segmentation","date":"2024-10-30","arxiv_id":"2410.23132","repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-hybrid-deep-learning-model-for","title":"Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images","date":"2024-10-29","arxiv_id":"2411.00832","repositories_listed":0,"syntology":null},{"url":null,"slug":"ka-2-er-knowledge-adaptive-amalgamation-of","title":"KA$^2$ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation","date":"2024-10-28","arxiv_id":"2410.21085","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multi-dimensional-explanation","title":"Towards Multi-dimensional Explanation Alignment for Medical Classification","date":"2024-10-28","arxiv_id":"2410.21494","repositories_listed":0,"syntology":null},{"url":null,"slug":"dct-histotransformer-efficient-lightweight","title":"DCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis","date":"2024-10-24","arxiv_id":"2410.19166","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-generative-models-for-3d-medical-image","title":"Deep Generative Models for 3D Medical Image Synthesis","date":"2024-10-23","arxiv_id":"2410.17664","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-efficient-and-scalable-kernel","title":"Private, Efficient and Scalable Kernel Learning for Medical Image Analysis","date":"2024-10-21","arxiv_id":"2410.15840","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-token-fusion-for-multi-view-medical","title":"Random Token Fusion for Multi-View Medical Diagnosis","date":"2024-10-21","arxiv_id":"2410.15847","repositories_listed":0,"syntology":null},{"url":null,"slug":"concept-complement-bottleneck-model-for","title":"Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis","date":"2024-10-20","arxiv_id":"2410.15446","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-approach-towards-the-classification","title":"A novel approach towards the classification of Bone Fracture from Musculoskeletal Radiography images using Attention Based Transfer Learning","date":"2024-10-18","arxiv_id":"2410.14833","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-applications-in-medical-image","title":"Deep Learning Applications in Medical Image Analysis: Advancements, Challenges, and Future Directions","date":"2024-10-18","arxiv_id":"2410.14131","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approach-to-brain-tumor","title":"Machine learning approach to brain tumor detection and classification","date":"2024-10-16","arxiv_id":"2410.12692","repositories_listed":0,"syntology":null},{"url":null,"slug":"unicon-universal-conditional-networks-for","title":"UniCoN: Universal Conditional Networks for Multi-Age Embryonic Cartilage Segmentation with Sparsely Annotated Data","date":"2024-10-16","arxiv_id":"2410.13043","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-real-artifacts-to-virtual-reference-a","title":"From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images","date":"2024-10-15","arxiv_id":"2410.13896","repositories_listed":0,"syntology":null},{"url":null,"slug":"eg-spikeformer-eye-gaze-guided-transformer-on","title":"EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis","date":"2024-10-12","arxiv_id":"2410.09674","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-image-quality-assessment-based-on","title":"Medical Image Quality Assessment based on Probability of Necessity and Sufficiency","date":"2024-10-10","arxiv_id":"2410.08118","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-brain-tumor-segmentation-an","title":"Federated brain tumor segmentation: an extensive benchmark","date":"2024-10-07","arxiv_id":"2410.17265","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-explainable-ai-for-medical-image","title":"Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks","date":"2024-10-03","arxiv_id":"2410.02331","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-attention-frequency-fusion-at-multi","title":"Dual-Attention Frequency Fusion at Multi-Scale for Joint Segmentation and Deformable Medical Image Registration","date":"2024-09-29","arxiv_id":"2409.19658","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-ct-gpt-generating-3d-radiology-reports","title":"3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models","date":"2024-09-28","arxiv_id":"2409.19330","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-sdice-an-index-for-assessing","title":"Introducing SDICE: An Index for Assessing Diversity of Synthetic Medical Datasets","date":"2024-09-28","arxiv_id":"2409.19436","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-deep-learning-based-segmentation-and","title":"Toward Deep Learning-based Segmentation and Quantitative Analysis of Cervical Spinal Cord Magnetic Resonance Images","date":"2024-09-28","arxiv_id":"2409.19354","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-strategies-in-deep-learning-for","title":"Segmentation Strategies in Deep Learning for Prostate Cancer Diagnosis: A Comparative Study of Mamba, SAM, and YOLO","date":"2024-09-24","arxiv_id":"2409.16205","repositories_listed":0,"syntology":null},{"url":null,"slug":"transukan-computing-efficient-hybrid-kan","title":"TransUKAN:Computing-Efficient Hybrid KAN-Transformer for Enhanced Medical Image Segmentation","date":"2024-09-23","arxiv_id":"2409.14676","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-unet-brain-tumor-image-segmentation","title":"Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism","date":"2024-09-20","arxiv_id":"2409.13626","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-overcomplete-convolutional-auto-encoder","title":"Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation","date":"2024-09-19","arxiv_id":"2409.13001","repositories_listed":0,"syntology":null},{"url":null,"slug":"efcm-efficient-fine-tuning-on-compressed","title":"EFCM: Efficient Fine-tuning on Compressed Models for deployment of large models in medical image analysis","date":"2024-09-18","arxiv_id":"2409.11817","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-unet-model-for-brain-tumor-image","title":"Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism","date":"2024-09-13","arxiv_id":"2409.08588","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-domain-incremental-learning-for","title":"Continual Domain Incremental Learning for Privacy-aware Digital Pathology","date":"2024-09-10","arxiv_id":"2409.06455","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-scanner-domain-shift-on-deep","title":"The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study","date":"2024-09-06","arxiv_id":"2409.04368","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-prompt-engineering-for-medical-vision","title":"Visual Prompt Engineering for Medical Vision Language Models in Radiology","date":"2024-08-28","arxiv_id":"2408.15802","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-adult-glioma-through-mri-a-review","title":"Exploring Adult Glioma through MRI: A Review of Publicly Available Datasets to Guide Efficient Image Analysis","date":"2024-08-27","arxiv_id":"2409.00109","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-everywhere-parameter-efficient","title":"Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training","date":"2024-08-27","arxiv_id":"2408.15011","repositories_listed":0,"syntology":null},{"url":null,"slug":"meddit-a-knowledge-controlled-diffusion","title":"MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient","date":"2024-08-22","arxiv_id":"2408.12236","repositories_listed":0,"syntology":null},{"url":null,"slug":"pi-att-topology-attention-for-segmentation","title":"PI-Att: Topology Attention for Segmentation Networks through Adaptive Persistence Image Representation","date":"2024-08-15","arxiv_id":"2408.08038","repositories_listed":0,"syntology":null},{"url":null,"slug":"inflocnet-enhanced-lung-infection","title":"InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learning","date":"2024-08-12","arxiv_id":"2408.06459","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-momentum-based-deep-learning","title":"A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation","date":"2024-08-11","arxiv_id":"2408.05692","repositories_listed":0,"syntology":null},{"url":null,"slug":"coboom-codebook-guided-bootstrapping-for","title":"CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning","date":"2024-08-08","arxiv_id":"2408.04262","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-adaptation-of-video-segmentation-to-3d","title":"Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2","date":"2024-08-08","arxiv_id":"2408.04762","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-sharpness-based-optimizers-improve","title":"Do Sharpness-based Optimizers Improve Generalization in Medical Image Analysis?","date":"2024-08-07","arxiv_id":"2408.04065","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-01026","title":"PINNs for Medical Image Analysis: A Survey","date":"2024-08-02","arxiv_id":"2408.01026","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-00348","title":"Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks","date":"2024-08-01","arxiv_id":"2408.00348","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-challenges-on-fairness-of-artificial","title":"Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications","date":"2024-07-24","arxiv_id":"2407.16953","repositories_listed":0,"syntology":null},{"url":"/paper/masks-and-manuscripts-advancing-medical-pre","slug":"masks-and-manuscripts-advancing-medical-pre","title":"Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring","date":"2024-07-23","arxiv_id":"2407.16264","repositories_listed":0,"syntology":null},{"url":null,"slug":"dataset-distillation-in-medical-imaging-a","title":"Dataset Distillation in Medical Imaging: A Feasibility Study","date":"2024-07-19","arxiv_id":"2407.14429","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-prostate-tumour-volumes-from","title":"Segmentation of Prostate Tumour Volumes from PET Images is a Different Ball Game","date":"2024-07-15","arxiv_id":"2407.10537","repositories_listed":0,"syntology":null},{"url":null,"slug":"sacnet-a-spatially-adaptive-convolution","title":"SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation","date":"2024-07-14","arxiv_id":"2407.10157","repositories_listed":0,"syntology":null},{"url":null,"slug":"pfps-prompt-guided-flexible-pathological","title":"PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models","date":"2024-07-13","arxiv_id":"2407.09979","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-domain-adaptation-model-for-carotid","title":"A Domain Adaptation Model for Carotid Ultrasound: Image Harmonization, Noise Reduction, and Impact on Cardiovascular Risk Markers","date":"2024-07-06","arxiv_id":"2407.05163","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-med3d-moe-towards-a-non-forgetting","title":"SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation","date":"2024-07-06","arxiv_id":"2407.04938","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-trustworthiness-in-foundation","title":"A Survey on Trustworthiness in Foundation Models for Medical Image Analysis","date":"2024-07-03","arxiv_id":"2407.15851","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-attention-integrated-deep-learning","title":"Exploiting Precision Mapping and Component-Specific Feature Enhancement for Breast Cancer Segmentation and Identification","date":"2024-07-03","arxiv_id":"2407.02844","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-branch-cnn-and-grouping-cascade","title":"Multi-branch CNN and grouping cascade attention for medical image classification","date":"2024-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mh-pflgb-model-heterogeneous-personalized","title":"MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis","date":"2024-06-29","arxiv_id":"2407.00474","repositories_listed":0,"syntology":null},{"url":null,"slug":"malaria-cell-detection-using-deep-neural","title":"Malaria Cell Detection Using Deep Neural Networks","date":"2024-06-28","arxiv_id":"2406.20005","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-classification-using-vision","title":"Brain Tumor Classification using Vision Transformer with Selective Cross-Attention Mechanism and Feature Calibration","date":"2024-06-25","arxiv_id":"2406.17670","repositories_listed":0,"syntology":null},{"url":null,"slug":"dwarf-disease-weighted-network-for-attention","title":"DWARF: Disease-weighted network for attention map refinement","date":"2024-06-24","arxiv_id":"2406.17032","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-artificial-intelligence-for-science","title":"Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars","date":"2024-06-24","arxiv_id":"2406.17812","repositories_listed":0,"syntology":null},{"url":null,"slug":"tp-drseg-improving-diabetic-retinopathy","title":"TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM","date":"2024-06-22","arxiv_id":"2406.15764","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-efficient-medical-image-analysis","title":"Resource-efficient Medical Image Analysis with Self-adapting Forward-Forward Networks","date":"2024-06-20","arxiv_id":"2406.14038","repositories_listed":0,"syntology":null},{"url":null,"slug":"head-pose-estimation-and-3d-neural-surface","title":"Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular Camera in situ for Navigation and Safe Insertion into Natural Openings","date":"2024-06-18","arxiv_id":"2406.13048","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-active-learning-framework-for","title":"Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification","date":"2024-06-17","arxiv_id":"2406.11310","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-medical-image-classification-with","title":"Boosting Medical Image Classification with Segmentation Foundation Model","date":"2024-06-16","arxiv_id":"2406.11026","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-of-foundation-models","title":"A Comprehensive Survey of Foundation Models in Medicine","date":"2024-06-15","arxiv_id":"2406.10729","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-pre-training-with-topology-and","title":"Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation","date":"2024-06-15","arxiv_id":"2406.10519","repositories_listed":0,"syntology":null}],"record_sha256":"826e69cd72553912123f6d89cd10ad78b1742f9aeb1504308472f0a53471f859","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}