{"total":435,"rows":[{"id":"2201.01266","title":"Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images","created":"2022-01-04","cited_by_count":830,"area":null},{"id":"2304.12620","title":"Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation","created":"2023-04-25","cited_by_count":490,"area":null},{"id":"2201.09873","title":"Transformers in Medical Imaging: A Survey","created":"2022-01-24","cited_by_count":245,"area":null},{"id":"2404.09556","title":"nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation","created":"2024-04-15","cited_by_count":240,"area":null},{"id":"2112.09045","title":"The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization","created":"2021-12-16","cited_by_count":178,"area":null},{"id":"2303.09975","title":"MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation","created":"2023-03-17","cited_by_count":162,"area":null},{"id":"2408.00874","title":"Medical SAM 2: Segment medical images as video via Segment Anything Model 2","created":"2024-08-01","cited_by_count":154,"area":null},{"id":"2210.11006","title":"SimpleClick: Interactive Image Segmentation with Simple Vision Transformers","created":"2022-10-20","cited_by_count":111,"area":null},{"id":"2310.15161","title":"SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images","created":"2023-10-23","cited_by_count":107,"area":null},{"id":"2304.06716","title":"STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training","created":"2023-04-13","cited_by_count":100,"area":null},{"id":"2304.06131","title":"UniverSeg: Universal Medical Image Segmentation","created":"2023-04-12","cited_by_count":91,"area":null},{"id":"2204.10836","title":"Federated Learning Enables Big Data for Rare Cancer Boundary Detection","created":"2022-04-22","cited_by_count":84,"area":null},{"id":"2306.11876","title":"BMAD: Benchmarks for Medical Anomaly Detection","created":"2023-06-20","cited_by_count":82,"area":null},{"id":"2310.09909","title":"Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis","created":"2023-10-15","cited_by_count":77,"area":null},{"id":"2304.06022","title":"SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything","created":"2023-04-12","cited_by_count":75,"area":null},{"id":"2403.12570","title":"Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images","created":"2024-03-19","cited_by_count":69,"area":null},{"id":"2405.18368","title":"The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI","created":"2024-05-28","cited_by_count":69,"area":null},{"id":"2312.17183","title":"Large-Vocabulary Segmentation for Medical Images with Text Prompts","created":"2023-12-28","cited_by_count":62,"area":null},{"id":"2406.10729","title":"A Comprehensive Survey of Foundation Models in Medicine","created":"2024-06-15","cited_by_count":56,"area":null},{"id":"2311.11969","title":"SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks","created":"2023-11-20","cited_by_count":55,"area":null},{"id":"2305.07642","title":"The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma","created":"2023-05-12","cited_by_count":53,"area":null},{"id":"2305.09666","title":"AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks","created":"2023-05-16","cited_by_count":52,"area":null},{"id":"2305.19369","title":"The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)","created":"2023-05-30","cited_by_count":51,"area":null},{"id":"2305.17033","title":"The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)","created":"2023-05-26","cited_by_count":49,"area":null},{"id":"2312.07381","title":"ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image","created":"2023-12-12","cited_by_count":48,"area":null},{"id":"2304.03493","title":"UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner","created":"2023-04-07","cited_by_count":47,"area":null},{"id":"2402.03526","title":"nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model","created":"2024-02-05","cited_by_count":45,"area":null},{"id":"2207.05282","title":"PseudoClick: Interactive Image Segmentation with Click Imitation","created":"2022-07-12","cited_by_count":42,"area":null},{"id":"2503.08373","title":"nnInteractive: Redefining 3D Promptable Segmentation","created":"2025-03-11","cited_by_count":41,"area":null},{"id":"2110.03352","title":"Optimized U-Net for Brain Tumor Segmentation","created":"2021-10-07","cited_by_count":40,"area":null},{"id":"2204.13738","title":"One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation","created":"2022-04-28","cited_by_count":40,"area":null},{"id":"2406.10395","title":"BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation","created":"2024-06-14","cited_by_count":39,"area":null},{"id":"2305.10300","title":"One-Prompt to Segment All Medical Images","created":"2023-05-17","cited_by_count":38,"area":null},{"id":"2112.04653","title":"Extending nn-UNet for brain tumor segmentation","created":"2021-12-09","cited_by_count":36,"area":null},{"id":"2410.09890","title":"Large-Scale 3D Medical Image Pre-training with Geometric Context Priors","created":"2024-10-13","cited_by_count":36,"area":null},{"id":"2310.14230","title":"A comprehensive survey on deep active learning in medical image analysis","created":"2023-10-22","cited_by_count":35,"area":null},{"id":"2411.01409","title":"Classifier-guided Gradient Modulation for Enhanced Multimodal Learning","created":"2024-11-03","cited_by_count":35,"area":null},{"id":"2206.01136","title":"Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives","created":"2022-06-02","cited_by_count":33,"area":null},{"id":"2112.06979","title":"The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients","created":"2021-12-13","cited_by_count":32,"area":null},{"id":"2308.16139","title":"MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision","created":"2023-08-30","cited_by_count":32,"area":null},{"id":"2411.03670","title":"Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?","created":"2024-11-06","cited_by_count":31,"area":null},{"id":"2501.11755","title":"A generalizable 3D framework and model for self-supervised learning in medical imaging","created":"2025-01-20","cited_by_count":31,"area":null},{"id":"2411.12814","title":"Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline","created":"2024-11-19","cited_by_count":30,"area":null},{"id":"2411.02372","title":"Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis","created":"2024-11-04","cited_by_count":29,"area":null},{"id":"2112.10074","title":"QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results","created":"2021-12-19","cited_by_count":28,"area":null},{"id":"2401.07654","title":"Foundation Models for Biomedical Image Segmentation: A Survey","created":"2024-01-15","cited_by_count":28,"area":null},{"id":"2402.17317","title":"How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation","created":"2024-02-27","cited_by_count":28,"area":null},{"id":"2412.03342","title":"UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection","created":"2024-12-04","cited_by_count":28,"area":null},{"id":"2404.04518","title":"MedIAnomaly: A comparative study of anomaly detection in medical images","created":"2024-04-06","cited_by_count":26,"area":null},{"id":"2412.17041","title":"An OpenMind for 3D medical vision self-supervised learning","created":"2024-12-22","cited_by_count":26,"area":null},{"id":"2207.07370","title":"CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation","created":"2022-07-15","cited_by_count":24,"area":null},{"id":"2303.00609","title":"Unsupervised Pathology Detection: A Deep Dive Into the State of the Art","created":"2023-03-01","cited_by_count":24,"area":null},{"id":"2305.09011","title":"The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)","created":"2023-05-15","cited_by_count":24,"area":null},{"id":"2407.16684","title":"AutoRG-Brain: Grounded Report Generation for Brain MRI","created":"2024-07-23","cited_by_count":24,"area":null},{"id":"2502.14064","title":"Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging","created":"2025-02-19","cited_by_count":24,"area":null},{"id":"2403.09303","title":"Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective","created":"2024-03-14","cited_by_count":23,"area":null},{"id":"2410.10604","title":"Multi-modal Vision Pre-training for Medical Image Analysis","created":"2024-10-14","cited_by_count":23,"area":null},{"id":"2404.00741","title":"Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts","created":"2024-03-31","cited_by_count":22,"area":null},{"id":"2109.12265","title":"Label-Assemble: Leveraging Multiple Datasets with Partial Labels","created":"2021-09-25","cited_by_count":20,"area":null},{"id":"2310.03485","title":"BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification","created":"2023-10-05","cited_by_count":20,"area":null},{"id":"2401.13650","title":"Tyche: Stochastic In-Context Learning for Medical Image Segmentation","created":"2024-01-24","cited_by_count":20,"area":null},{"id":"2405.08431","title":"Similarity and Quality Metrics for MR Image-To-Image Translation","created":"2024-05-14","cited_by_count":20,"area":null},{"id":"2109.12271","title":"BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation","created":"2021-09-25","cited_by_count":19,"area":null},{"id":"2212.09310","title":"Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution","created":"2022-12-19","cited_by_count":17,"area":null},{"id":"2408.00640","title":"AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation","created":"2024-08-01","cited_by_count":17,"area":null},{"id":"2410.21348","title":"Large Language Model Benchmarks in Medical Tasks","created":"2024-10-28","cited_by_count":17,"area":null},{"id":"2204.00631","title":"UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation","created":"2022-04-01","cited_by_count":16,"area":null},{"id":"2111.00742","title":"Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs","created":"2021-11-01","cited_by_count":15,"area":null},{"id":"2405.09787","title":"Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge","created":"2024-05-16","cited_by_count":15,"area":null},{"id":"2211.06004","title":"A Comprehensive Survey of Transformers for Computer Vision","created":"2022-11-11","cited_by_count":14,"area":null},{"id":"2404.15009","title":"The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)","created":"2024-04-23","cited_by_count":14,"area":null},{"id":"2405.10246","title":"A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts","created":"2024-05-16","cited_by_count":14,"area":null},{"id":"2503.16247","title":"OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection","created":"2025-03-20","cited_by_count":14,"area":null},{"id":"2505.21862","title":"Towards Scalable Language-Image Pre-training for 3D Medical Imaging","created":"2025-05-28","cited_by_count":14,"area":null},{"id":"2412.15058","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","created":"2024-12-19","cited_by_count":13,"area":null},{"id":"2206.11739","title":"Evidence fusion with contextual discounting for multi-modality medical image segmentation","created":"2022-06-23","cited_by_count":12,"area":null},{"id":"2303.10333","title":"HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation","created":"2023-03-18","cited_by_count":12,"area":null},{"id":"2403.09262","title":"Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks","created":"2024-03-14","cited_by_count":12,"area":null},{"id":"2502.20985","title":"LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging","created":"2025-02-28","cited_by_count":12,"area":null},{"id":"2302.00517","title":"Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI","created":"2023-02-01","cited_by_count":11,"area":null},{"id":"2305.00005","title":"The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)","created":"2023-04-27","cited_by_count":11,"area":null},{"id":"2305.04276","title":"AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation","created":"2023-05-07","cited_by_count":11,"area":null},{"id":"2407.08855","title":"BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023","created":"2024-07-11","cited_by_count":11,"area":null},{"id":"2603.07952","title":"VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer","created":"2026-03-09","cited_by_count":11,"area":null},{"id":"2404.04971","title":"FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation","created":"2024-04-07","cited_by_count":10,"area":null},{"id":"2407.12474","title":"Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection","created":"2024-07-17","cited_by_count":10,"area":null},{"id":"2409.17055","title":"DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data","created":"2024-09-25","cited_by_count":10,"area":null},{"id":"2506.23810","title":"MadCLIP: Few-shot Medical Anomaly Detection with CLIP","created":"2025-06-30","cited_by_count":10,"area":null},{"id":"2201.03777","title":"Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task","created":"2022-01-11","cited_by_count":9,"area":null},{"id":"2204.14199","title":"Preoperative brain tumor imaging: models and software for segmentation and standardized reporting","created":"2022-04-29","cited_by_count":9,"area":null},{"id":"2303.13399","title":"Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation","created":"2023-03-23","cited_by_count":9,"area":null},{"id":"2304.07248","title":"The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset","created":"2023-04-14","cited_by_count":9,"area":null},{"id":"2307.00885","title":"An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis","created":"2023-07-03","cited_by_count":9,"area":null},{"id":"2405.12963","title":"Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma","created":"2024-05-21","cited_by_count":9,"area":null},{"id":"2406.02422","title":"IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRI","created":"2024-06-04","cited_by_count":9,"area":null},{"id":"2407.14651","title":"Improving Representation of High-frequency Components for Medical Visual Foundation Models","created":"2024-07-19","cited_by_count":9,"area":null},{"id":"2510.23415","title":"Towards Generalisable Foundation Models for Brain MRI","created":"2025-10-27","cited_by_count":9,"area":null},{"id":"2201.08582","title":"SegTransVAE: Hybrid CNN -- Transformer with Regularization for medical image segmentation","created":"2022-01-21","cited_by_count":8,"area":null},{"id":"2207.11553","title":"High-Resolution Swin Transformer for Automatic Medical Image Segmentation","created":"2022-07-23","cited_by_count":8,"area":null},{"id":"2305.08992","title":"The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting","created":"2023-05-15","cited_by_count":8,"area":null}],"next_cursor":"100"}