Medical Image Analysis Using Deep Learning: A Systematic Literature ReviewThe field of big data analytics has started playing a vital role in the advancement of Medical Image Analysis (MIA) over the last decades very quickly. Healthcare is a major example of how the three Vs of data i.e.,...
综述标题:Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions 论文链接:[2303.12484] Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions (arxiv.org) 摘要: 近年来,深度学习发展迅速,并在广泛的应用中取得了最先进的性能。然而,训练模...
今天要跟大家分享的是关于医学图像分割方法的综述,我们将翻译一篇2020年的医学图像分割综述文章,题为“Medical Image Segmentation Using Deep Learning: A Survey”,该文章介绍了深度学习在医学图像分割领域的应用和发展情况。 一、简介(一)医学图像分割 一般的图像分割任务主要有两类:语义分割(semantic segmentation)和...
Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which ...
3.Deep learning uses in medical imaging 3.1 Classification 3.1.1 Image/exam classification 因为数据集较小(成百上千),迁移学习的普及。 迁移学习本质上是使用预训练的网络(通常在自然图像上)来尝试解决对大型数据集进行(感知)的深度网络训练的需求。确定了两种迁移学习策略:(1)使用预先训练的网络作为特征提取器...
Download: Download full-size image Fig. 3. The multi-modal medical images, (a)–(c) are the commonly used multi-modal medical images and (d)–(g) are the different sequences of brain MRI. There are also some other reviews on medical image analysis using deep learning. However, they do...
Deep Learning in Medical Image Analysis 5 Fig. 1 Literature search for publications in peer-reviewed journals by Web of Science from 1900 to early July of 2019 using key words: ((imaging OR images) AND (medical OR diagnostic)) AND (machine learning OR deep learning OR neural network OR dee...
medical image analysis based on DLA.Keywords Deeplearning . Convolutionalneuralnetworks . Medicalimages . Segmentation .Classification . Detection1 IntroductionIn the health care system, there has been a dramatic increase in demand for medical imageservices, e.g. Radiography, endoscopy, Computed ...
文章首先介绍了近些年来深度学习领域的发展,包括CNN、DCNN、RNN以及一些无监督弱监督的方式,比如自编码器等。然后介绍医学影像不同领域的发展。 首先,分类领域。膝关节骨性关节炎多级评分,使用CNN作为特征提取器的细胞病理学图像分类,应用DBN和SAE将患者分类为基于脑部磁共振成像(MRI)的阿尔茨海默病,皮肤损伤图像,多流...
Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning (http://t.cn/RWADYxw) Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks (http://t.cn/RWADk5G) A Deep Semantic Mobile Application for Thyroid Cytopathology (http://t.cn/RWAko5r...