{"total":544,"rows":[{"id":"2304.13785","title":"Customized Segment Anything Model for Medical Image Segmentation","created":"2023-04-26","cited_by_count":280,"area":null},{"id":"2102.08005","title":"TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation","created":"2021-02-16","cited_by_count":258,"area":null},{"id":"2201.09873","title":"Transformers in Medical Imaging: A Survey","created":"2022-01-24","cited_by_count":245,"area":null},{"id":"2103.03024","title":"CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation","created":"2021-03-04","cited_by_count":227,"area":null},{"id":"2301.00785","title":"CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection","created":"2023-01-02","cited_by_count":224,"area":null},{"id":"2211.14830","title":"Medical Image Segmentation Review: The success of U-Net","created":"2022-11-27","cited_by_count":191,"area":null},{"id":"2106.00487","title":"Dense Nested Attention Network for Infrared Small Target Detection","created":"2021-06-01","cited_by_count":135,"area":null},{"id":"2111.11843","title":"U-shape Transformer for Underwater Image Enhancement","created":"2021-11-23","cited_by_count":112,"area":null},{"id":"2405.12971","title":"BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once","created":"2024-05-21","cited_by_count":111,"area":null},{"id":"2105.13381","title":"Recent advances and clinical applications of deep learning in medical image analysis","created":"2021-05-27","cited_by_count":104,"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":"2209.15076","title":"3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation","created":"2022-09-29","cited_by_count":94,"area":null},{"id":"2203.14291","title":"Video Polyp Segmentation: A Deep Learning Perspective","created":"2022-03-27","cited_by_count":82,"area":null},{"id":"2207.14191","title":"Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation","created":"2022-07-28","cited_by_count":76,"area":null},{"id":"2306.06370","title":"AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder","created":"2023-06-10","cited_by_count":70,"area":null},{"id":"2403.13430","title":"MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining","created":"2024-03-20","cited_by_count":66,"area":null},{"id":"2205.12853","title":"Deep Gradient Learning for Efficient Camouflaged Object Detection","created":"2022-05-25","cited_by_count":65,"area":null},{"id":"2408.08870","title":"SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation","created":"2024-08-16","cited_by_count":64,"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":"2110.08762","title":"Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation","created":"2021-10-17","cited_by_count":61,"area":null},{"id":"2304.07583","title":"Can SAM Segment Polyps?","created":"2023-04-15","cited_by_count":59,"area":null},{"id":"2303.14869","title":"Label-Free Liver Tumor Segmentation","created":"2023-03-27","cited_by_count":56,"area":null},{"id":"2307.15615","title":"A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond","created":"2023-07-28","cited_by_count":55,"area":null},{"id":"2105.07451","title":"MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation","created":"2021-05-16","cited_by_count":53,"area":null},{"id":"2306.13731","title":"How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images","created":"2023-06-23","cited_by_count":53,"area":null},{"id":"2107.05023","title":"NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection","created":"2021-07-11","cited_by_count":51,"area":null},{"id":"2103.15954","title":"DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation","created":"2021-03-29","cited_by_count":48,"area":null},{"id":"2304.09324","title":"Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets","created":"2023-04-18","cited_by_count":45,"area":null},{"id":"2204.12077","title":"AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images","created":"2022-04-26","cited_by_count":43,"area":null},{"id":"2111.03216","title":"Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network","created":"2021-11-05","cited_by_count":42,"area":null},{"id":"2107.05188","title":"TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentation","created":"2021-07-12","cited_by_count":41,"area":null},{"id":"2212.00990","title":"Feature Aggregation and Propagation Network for Camouflaged Object Detection","created":"2022-12-02","cited_by_count":40,"area":null},{"id":"2111.06061","title":"Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI","created":"2021-11-11","cited_by_count":39,"area":null},{"id":"2103.17235","title":"FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation","created":"2021-03-31","cited_by_count":36,"area":null},{"id":"2307.03942","title":"Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images","created":"2023-07-08","cited_by_count":34,"area":null},{"id":"2309.03493","title":"SAM3D: Segment Anything Model in Volumetric Medical Images","created":"2023-09-07","cited_by_count":34,"area":null},{"id":"2205.05335","title":"Deep Depth Completion from Extremely Sparse Data: A Survey","created":"2022-05-11","cited_by_count":33,"area":null},{"id":"2303.06274","title":"CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting","created":"2023-03-11","cited_by_count":33,"area":null},{"id":"2101.03244","title":"End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction","created":"2021-01-08","cited_by_count":32,"area":null},{"id":"2207.13362","title":"Camouflaged Object Detection via Context-aware Cross-level Fusion","created":"2022-07-27","cited_by_count":32,"area":null},{"id":"2308.01239","title":"CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion","created":"2023-08-02","cited_by_count":31,"area":null},{"id":"2405.18356","title":"Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography","created":"2024-05-28","cited_by_count":31,"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":"2109.05676","title":"Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation","created":"2021-09-13","cited_by_count":30,"area":null},{"id":"2206.09309","title":"TBraTS: Trusted Brain Tumor Segmentation","created":"2022-06-19","cited_by_count":30,"area":null},{"id":"2203.15177","title":"Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation","created":"2022-03-29","cited_by_count":28,"area":null},{"id":"2309.03329","title":"MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation","created":"2023-09-06","cited_by_count":28,"area":null},{"id":"2202.12031","title":"Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge","created":"2022-02-24","cited_by_count":27,"area":null},{"id":"2202.10336","title":"Artificial Intelligence for the Metaverse: A Survey","created":"2022-02-15","cited_by_count":25,"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":"2402.02029","title":"ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation","created":"2024-02-03","cited_by_count":24,"area":null},{"id":"2107.14599","title":"SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection","created":"2021-07-30","cited_by_count":23,"area":null},{"id":"2112.02508","title":"Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation","created":"2021-12-05","cited_by_count":22,"area":null},{"id":"2402.16663","title":"UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images","created":"2024-02-26","cited_by_count":22,"area":null},{"id":"2105.07467","title":"Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy","created":"2021-05-16","cited_by_count":20,"area":null},{"id":"2310.07781","title":"3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers","created":"2023-10-11","cited_by_count":20,"area":null},{"id":"2403.06459","title":"From Pixel to Cancer: Cellular Automata in Computed Tomography","created":"2024-03-11","cited_by_count":20,"area":null},{"id":"2308.14936","title":"AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation","created":"2023-08-28","cited_by_count":19,"area":null},{"id":"2107.04823","title":"BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images","created":"2021-07-10","cited_by_count":17,"area":null},{"id":"2209.00729","title":"HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images","created":"2022-09-01","cited_by_count":17,"area":null},{"id":"2303.10396","title":"Towards Diverse Binary Segmentation via A Simple yet General Gated Network","created":"2023-03-18","cited_by_count":17,"area":null},{"id":"2307.09039","title":"PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networks","created":"2023-07-18","cited_by_count":17,"area":null},{"id":"2309.13646","title":"ILNet: Low-level Matters for Salient Infrared Small Target Detection","created":"2023-09-24","cited_by_count":17,"area":null},{"id":"2409.00924","title":"MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM","created":"2024-09-02","cited_by_count":17,"area":null},{"id":"2304.00212","title":"Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization","created":"2023-04-01","cited_by_count":16,"area":null},{"id":"2308.09917","title":"Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation","created":"2023-08-19","cited_by_count":16,"area":null},{"id":"2310.01861","title":"Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos","created":"2023-10-03","cited_by_count":16,"area":null},{"id":"2406.01076","title":"Estimating Canopy Height at Scale","created":"2024-06-03","cited_by_count":16,"area":null},{"id":"2411.04656","title":"ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism","created":"2024-11-07","cited_by_count":16,"area":null},{"id":"2508.20909","title":"Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation","created":"2025-08-28","cited_by_count":16,"area":null},{"id":"2107.00875","title":"LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos","created":"2021-07-02","cited_by_count":15,"area":null},{"id":"2203.17070","title":"Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes","created":"2022-03-31","cited_by_count":15,"area":null},{"id":"2301.12291","title":"CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans","created":"2023-01-28","cited_by_count":15,"area":null},{"id":"2107.04263","title":"Towards Robust General Medical Image Segmentation","created":"2021-07-09","cited_by_count":14,"area":null},{"id":"2112.10652","title":"HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet","created":"2021-12-20","cited_by_count":14,"area":null},{"id":"2210.14845","title":"Synthetic Tumors Make AI Segment Tumors Better","created":"2022-10-26","cited_by_count":14,"area":null},{"id":"2211.13238","title":"ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans","created":"2022-11-23","cited_by_count":14,"area":null},{"id":"2405.06822","title":"MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis","created":"2024-05-10","cited_by_count":14,"area":null},{"id":"2409.00346","title":"SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation","created":"2024-08-31","cited_by_count":14,"area":null},{"id":"2410.09909","title":"UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation","created":"2024-10-13","cited_by_count":14,"area":null},{"id":"2410.17241","title":"Frontiers in Intelligent Colonoscopy","created":"2024-10-22","cited_by_count":14,"area":null},{"id":"2205.04846","title":"MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation","created":"2022-05-10","cited_by_count":13,"area":null},{"id":"2208.06991","title":"Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers","created":"2022-08-15","cited_by_count":13,"area":null},{"id":"2209.07193","title":"Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation","created":"2022-09-15","cited_by_count":13,"area":null},{"id":"2303.10894","title":"M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation","created":"2023-03-20","cited_by_count":13,"area":null},{"id":"2403.08689","title":"Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images","created":"2024-03-13","cited_by_count":13,"area":null},{"id":"2510.14831","title":"Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data","created":"2025-10-16","cited_by_count":13,"area":null},{"id":"2107.12461","title":"Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation","created":"2021-07-26","cited_by_count":12,"area":null},{"id":"2203.14928","title":"RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging","created":"2022-03-28","cited_by_count":12,"area":null},{"id":"2308.12231","title":"SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation","created":"2023-08-23","cited_by_count":12,"area":null},{"id":"2309.16967","title":"nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance","created":"2023-09-29","cited_by_count":12,"area":null},{"id":"2310.19898","title":"MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder","created":"2023-10-30","cited_by_count":12,"area":null},{"id":"2311.10696","title":"Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation","created":"2023-11-17","cited_by_count":12,"area":null},{"id":"2403.16646","title":"Clustering Propagation for Universal Medical Image Segmentation","created":"2024-03-25","cited_by_count":12,"area":null},{"id":"2111.10614","title":"GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation","created":"2021-11-20","cited_by_count":11,"area":null},{"id":"2206.01793","title":"R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation","created":"2022-06-03","cited_by_count":11,"area":null},{"id":"2212.06378","title":"Robust Split Federated Learning for U-shaped Medical Image Networks","created":"2022-12-13","cited_by_count":11,"area":null},{"id":"2304.06662","title":"Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions","created":"2023-04-13","cited_by_count":11,"area":null},{"id":"2306.10940","title":"TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting","created":"2023-06-19","cited_by_count":11,"area":null},{"id":"2307.09787","title":"DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image Analysis","created":"2023-07-19","cited_by_count":11,"area":null}],"next_cursor":"100"}