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 attempts to use the knowledge learned from one task and apply it to improve the learning of a separate but similar task. This article proposes to evaluate this technique’s effectiveness in classifying images from the medical domain. The article presents a model TrFEMNet (Transfer...
《Medical image classification using synergic deep learning》论文笔记 利用协同深度学习进行医学图像分类 0 Abstract 医学图像分类在计算机辅助诊断、医学图像检索和医学图像挖掘中是一个非常重要的任务。尽管深度学习相对于传统的手工标注特征的方法有明显的优势,但是因为成像方式和临床病理造成的类内差异和类间相似性,...
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...
learning models for image analysis.Keywords: Transfer learning · Contrastive learning · SimSiam · BiT.1 IntroductionLarge-scale annotated medical images are typically more diff icult to achieve comparedwithnaturalimages,limitedbydomainknowledgeandpotentialprivacyconstraints[12,27]. As a result, numerous...
Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scar
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 ...
Transfer learning is widely used in computer vision and natural language processing (NLP). The following examples show how these technologies can be used to improve health and well-being. Medical Imaging The latest computer vision technologies are transforming medical imaging by streamlining workflows ...
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 ...