Medical image analysisImaging modalitiesThe advent of deep learning has brought great change to the community of computer science and also revitalized numerous fields where traditional machine learning methods failed to make breakthroughs. Benefitted from the development of deep learning, analysis of ...
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
Transfer learningWe present a critical assessment of the role of transfer learning in training fully convolutional networks (FCNs) for medical image segmentation. We first show that although transfer learning reduces the training time on the target task, improvements in segmentation accuracy are highly ...
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’s practicality extends to diverse domains, revolutionizing industries with efficiency and performance enhancement. Here are real-life applications and illustrative examples: Healthcare: Medical Image Analysis Transfer learning improves medical image analysis. For instance, a model trained on...
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...
《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,...
There have been attempts that use deep learning methods to automatically diagnose COVID-19 based on CT scan images, which reduce burden for medical experts and enable fast diagnosis. However, due to the lack of publicly available CT scan data for COVID-19, it is difficult to devise a good...
Increasing attention to the deep learning applications comes to the medical image analysis as well. Recently, Google published the paper on detecting the diabetic retinopathy using their deep learning approach. During our doctoral research, we will mainly focus on dealing with medical image dataset fro...
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. This study proposes a novel training framework...