louaq 
置顶
RCPS Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which requires a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss in the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed
method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation.
置顶
Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation In semi-supervised medical image segmentation,most previousworks drawon the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation.
Mutual learning with reliable pseudo label for semi-supervised medical image segmentation
Semi-supervised learning has garnered significant interest as a method to alleviate the burden of data
annotation. Recently, semi-supervised medical image segmentation has garnered significant interest that can
alleviate the burden of densely annotated data. Substantial advancements have been achieved by integrating
consistency-regularization and pseudo-labeling techniques. The quality of the pseudo-labels is crucial in this
regard. Unreliable pseudo-labeling can result in the introduction of noise, leading the model to converge to
suboptimal solutions. To address this issue, we propose learning from reliable pseudo-labels. In this paper,
we tackle two critical questions in learning from reliable pseudo-labels: which pseudo-labels are reliable and
how reliable are they? Specifically, we conduct a comparative analysis of two subnetworks to address both
challenges. Initially, we compare the prediction confidence of the two subnetworks. A higher confidence score
indicates a more reliable pseudo-label. Subsequently, we utilize intra-class similarity to assess the reliability
of the pseudo-labels to address the second challenge. The greater the intra-class similarity of the predicted
classes, the more reliable the pseudo-label. The subnetwork selectively incorporates knowledge imparted by
the other subnetwork model, contingent on the reliability of the pseudo labels. By reducing the introduction of
noise from unreliable pseudo-labels, we are able to improve the performance of segmentation. To demonstrate
the superiority of our approach, we conducted an extensive set of experiments on three datasets: Left Atrium,
Pancreas-CT and Brats-2019. The experimental results demonstrate that our approach achieves state-of-the-art
performance. Code is available at: https://github.com/Jiawei0o0/mutual-learning-with-reliable-pseudo-labels
Windows安装mamba
windows系统下安装mamba会遇到各种各样的问题。博主试了好几天，把能踩的坑都踩了，总结出了在windows下安装mamba的一套方法，已经给实验室的windows服务器都装上了。只要跟着我的流程走下来，大概率不会出问题，如果遇到其他问题，可以在评论区讨论，我会的我会回复。
首先创建mamba的环境，然后安装必要的库。请你创建一个新环境，而不是用以前的环境，版本这些就跟着这个里面来。
BraTs2023数据集处理及python读取.nii文件
BraTS2023-MEN(Brain Tumor Segmentation 2023 Meningioma Challenge) 是 BraTS2023 五个分割子任务中之一，与 BraTS 常规分割脑胶质瘤不同，该子任务目标是从多模态 MR 图像 (mpMRI) 中分割脑膜瘤。该数据集在 23 年 5 月份放出合计 6 个中心的 1650 例数据，其中有标注的训练集 1000 例，每例提供四种序列 MR 的输入图像（t1w, t1c, t2w, t2f）以及脑膜瘤的分割结果，标注内容主要包括非增强肿瘤核心（NETC）、周围非增强的FLAIR高信号（SNFH）和增强型肿瘤（ET）。验证集提供图像但没有标注，可以在官网提交验证，而测试集数据不公开。