共计 31 篇文章
2025
0-TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM
0-Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation
0-SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image
0-SAM-SP: Self-Prompting Makes SAM Great Again
0-Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts
0-TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images
0-SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT
2024
0-One-Prompt to Segment All Medical Images
0-Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes
0-DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation
0-Dr-SAM: U-Shape Structure Segment Anything Model for Generalizable Medical Image Segmentation
0-Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
0-SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
0-Efficient and Robust Medical Image Segmentation Using Lightweight ViT-Tiny based SAM and Model Quantization>
0-MAFE-Net: retinal vessel segmentation based on a multiple attention-guided fusion mechanism and ensemble learning network
0-LeSAM: Adapt Segment Anything Model for Medical Lesion Segmentation
0-GlanceSeg: Real-time microangioma lesion segmentation with gaze map-guided foundation model for early detection of diabetic retinopathy
0-RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning
0-FS-MedSAM2: Exploring the Potential of SAM2 for Few-Shot Medical Image Segmentation without Fine-tuning
0-Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images.
0-SAM2POINT: SEGMENT ANY 3D AS VIDEOS IN ZERO-SHOT AND PROMPTABLE MANNERS
0-ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation
0-Learnable Ophthalmology SAM
0-MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation
0-MEDICAL SAM 2: SEGMENT MEDICAL IMAGES AS VIDEO VIA SEGMENT ANYTHING MODEL 2
0-SAM-UNet: Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images
0-SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation
0-Segment Anything in Medical Images
0-Segment Anything Model for Medical Images
0-3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
0-Segment Anything