由于不是专门做few-shot learning,如有错误,请留言指正。 code: 尚未公开训练代码,只公开了测试代码。 GitHub - uci-cbcl/RP-Net: Code for Recurrent Mask Refinement for Few-Shot Medical Image Segmentation (ICCV 2021).github.com/uci-cbcl/RP-Net 参考文献: [1] Tang H , Liu X , Sun S , e...
Medical imageFew-shot learningSelf-supervised learningFew-shot segmentationMachine learningDeep learning-based segmentation models often struggle to achieve optimal performance when encountering new, unseen semantic classes. Their effectiveness hinges on vast amounts of annotated data and high computational ...
Despite the great progress made by deep convolutional neural networks (CNN) in medical image segmentation, they typically require a large amount of expert-level accurate, densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot learning has thus been...
[医学图像小样本分割]A LOCATION-SENSITIVE LOCAL PROTOTYPE NETWORK FOR FEW-SHOT MEDICAL IMAGE SEGMENTATION,程序员大本营,技术文章内容聚合第一站。
Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning Tianang Leng* Huazhong University of Science and Technology Wuhan, China tianangl@hust.edu.cn Yiming Zhang* Tokyo Institute of Technology Tokyo, Japan zhang.y.bl@m...
supervised learning model in computer vision. Leveraging the strengths of ALPNet and harnessing the feature extraction capabilities of DINOv2, we present a novel approach to few-shot segmentation that not only enhances performance but also paves the way for more robust and adaptable medical image ...
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data. While asking domain experts to annot... D Tomar,B Bozorgtabar,M Lortkipanidze,... 被引量: 0发...
Paper tables with annotated results for Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation
The Implementation of Paper: Partition A Medical Image: Extracting Multiple Representative Sub-Regions for Few-Shot Medical Image Segmentation AbstractFew-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally ...
Few Shot Medical Image Segmentation with Cross Attention Transformer Yi Lin*, Yufan Chen*, Kwang-Ting Cheng, Hao Chen Highlights In this work, we propose a novel framework for few-shot medical image segmentation, termed CAT-Net, based on cross masked attention Transformer. Our proposed network ...