Objective: Employing transfer learning (TL) with convolutional neural networks (CNNs), well-trained on non-medical ImageNet dataset, has shown promising results for medical image analysis in recent years. We aimed to conduct a scoping review to identify these studies and summarize their ...
Transfer learning is a pivotal concept in machine learning where knowledge gained from a pre-existing model is harnessed to enhance the performance of a new, related task. Instead of starting from scratch, transfer learning empowers developers to leverage the insights, features, and representations a...
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 ...
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learning in 3D medical images. However, it is extremely challenging to ...
Deep learning is being employed in disease detection and classification based on medical images for clinical decision making. It typically requires large amounts of labelled data; however, the sample size of such medical image datasets is generally small
In medical image analysis, we find that model fine-tuning plays a crucial role in adapting medical knowledge to target tasks. We propose a meta-learning-based LR tuner, MetaLR, to make different layers efficiently co-adapt to downstream tasks according to their transferabilities across different ...
今天分享一篇发表在CVPR 2020上的论文:LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation (原文链接:[1])。 1 研究背景 近年来随着深度学习的快速发展,深度卷积神经网络 (DCNNs)在许多分割任务上取得很好的性能。但是对于3D医学图像分割任务,获得3D空...
Transfer learning is to improve learning process in new tasks by transferring knowledge from related tasks that have already been learned32,33,34. In recent years, transfer learning has been gradually applied to many fields of medical image analysis, such as image segmentation, lesion localization,...
a target domain, where the required resources for training the model from scratch might not be available. A large number of pretrained models exist forimage processingtasks. Therefore, transfer learning has been widely used formedical image analysis, where acquiring large datasets is often impractical...
Transfer learning for zero-shot reasoning 迁移学习用于零次常识推理 ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation Fewshot UDA for medical image segmentation 小样本域自适应用于医疗图像分割 One Ring to Bring Them All: Towards Open-Set Recognition under Domain Sh...