共计 56 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-Fundus2Angio: A Conditional GAN Architecture for Generating Fluorescein Angiography Images from Retinal Fundus Photography
0-SAM2POINT: SEGMENT ANY 3D AS VIDEOS IN ZERO-SHOT AND PROMPTABLE MANNERS
0-A novel attention-guided convolutional network for the detection of
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
C-Vision Transformer (ViT)
0-Segment Anything
0-Diffusion Models Beat GANs on Image Synthesis
0-Why Should I Trust You Explaining the Predictions of Any Classifier
0-Dropout: A Simple Way to Prevent Neural Networks from Overfitting
0-Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or −1
0-Decoupled Neural Interfaces using Synthetic Gradients
0-Layer Normalization
0-HyKGE AHypothesis Knowledge Graph Enhanced Framework
0-Network Morphism
0-Net2Net: Accelerating Learning Via Knowledge Transfer
0-Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
0-Improving neural networks by preventing coadaptation of feature detectors
0-Going Deeper with Convolutions
0-SimCSE: Simple Contrastive Learning of Sentence Embeddings
0-Knowledge Mining with Scene Text for Fine-Grained Recognition
0-Instance and Panoptic Segmentation Using Conditional Convolutions
0-Faster R-CNN
0-CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation
0-ImageNet Classification with Deep Convolutional Neural Networks
0-Very Deep Convolutional Networks For Large-Scale Image Recognition
0-Fast R-CNN
0-Rich feature hierarchies for accurate object detection and semantic segmentation
0-Deep Residual Learning for Image Recognition