ConVIRT中的image encoder的参数是ImageNet初始化的,而CLIP直接用random初始化; ConVIRT的projection head是non-linear的,而CLIP采用linear的projection;(ConVIRT在后面的实验中也提到将non-linear换做linear,模型效果会下降;但CLIP中则说二者没有区别。) CLIP去掉了ConVIRT中text transformation(指均匀从text中采样句子);...
We formulate the 3D medical image classification as a Multiple Instance Learning (MIL) problem and introduce an attention-based MIL module to integrate the 2D instance features of each slice into the 3D feature for classification. Then, we simultaneously consider volume-based and slice-based ...
Contrastive Learning Meets Transfer Learning:A Case Study In Medical Image AnalysisYuzhe Lu, Aadarsh Jha, and Yuankai HuoComputer Science, Vanderbilt University,Nashville TN 37235, USAAbstract. Annotated medical images are typically rarer than labeled natural im-ages, since they are limited by domain ...
Data augmentation, self-learning, and contrastive learning are recognized for their potential in enhancing medical image segmentation, particularly in scenarios with limited annotated data. However, despite their promise, significant gaps persist in this field. One prominent gap is the need for a compre...
including the limited label informa-tion in the pre-training phase, it is possible to boost the performance ofcontrastive learning. We propose a supervised local contrastive loss thatleverages limited pixel-wise annotation to force pixels with the same la-bel to gather around in the embedding ...
2.2 Supervised Local Contrastive learning 设f l(xi) = h2(Dl(E(ai))是增强输入ai的第l个最上层解码器块Dl的输出特征映射,其中head h2是一个两层逐点卷积。对于feature map f (ai),局部对比损耗定义为, f属于rc是ai的feature map,c是通道数,f代表第u行第v列特征。Ω是f中的点, P (u, v)和N ...
Image analysisMachine learningAnnotated medical images are typically more rare than labeled natural images, since they are limited by domain knowledge and privacy constraints. Recent advances in transfer and contrastive learning have provided effective solutions to tackle such issues from different ...
Code for CVPR 2024 paper, "VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis" Authors: Linshan Wu, Jiaxin Zhuang, and Hao Chen This work presents VoCo, a simple-yet-effective contrastive learning framework for pre-training large scale 3D medical ima...
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask. To increase the ability of encoder-decoder neural networks to perform this task across large clinical cohorts, contrastive learning provides an opportunity to...
Code for CVPR 2024 paper, "VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis" Authors: Linshan Wu, Jiaxin Zhuang, and Hao Chen This work presents VoCo, a simple-yet-effective contrastive learning framework for pre-training large scale 3D medical ima...