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...
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。
基于认知科学以及《Prototypical Networks for Few-shot Learning》中的PN的原型理论,作者模仿之前的使用双分支架构,提出了一种小样本语义分割框架。双分支的第一分支是原型学习器,从support set中提取出对应各个类的原型特征向量;第二分支是分割网络,他将图片与原型特征向量作为输入,并且输出segmentation mask。 figure....
However, a promising solution to mitigate these challenges is the adoption of few-shot segmentation (FSS) networks, which can train models with reduced annotated data. The inherent complexity of medical images limits the applicability of FSS in medical imaging, despite its potential. Recent ...
下面将介绍几个基本的架构,这些架构为few shot segmentation的进展奠定了基础。 1.单样本学习 单样本学习是few shot segmentation的最基础方法之一。这种方法基于一个假设,即给定一个样本图像和其对应的分割掩码,模型可以通过学习到的特征将新的图像进行分割。具体而言,单样本学习可以分为两个阶段:特征提取和分割预测。
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 mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and ...
[IEEE TIM 2024] Partition A Medical Image: Extracting Multiple Representative Sub-Regions for Few-shot Medical Image Segmentation - YazhouZhu19/Partition-A-Medical-Image
论文链接:Few-Shot Semantic Segmentation with Prototype Learning 文章贡献 (1) 第一个提出 N-way k-shot 语义分割问题 (2) 我们提出了一个基于原型的框架,该框架对于小样本语义分割任务是有效的; (3) 提出了一些技术来解决训练过程中的过拟合问题; ...
prior \; mask的生成方式如论文Priorguidedfeatureenrichmentnetworkforfew−shotsegmentation中所示,主要是通过计算query像素特征和support像素特征之间的相似度,并将每一个相似度进行归一化到[0:1]。 模型操作对象:visionfeature并将其进行进一步融合。 Transformer模块应用:采用residual机制,起到增强分类器效果的作用。