Deep learning for chest X-ray analysis: A survey.
TLDR
In this article, a review of deep learning on chest X-ray images is presented, focusing on image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation.About:
This article is published in Medical Image Analysis.The article was published on 2021-06-05 and is currently open access. It has received 121 citations till now.read more
Citations
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Journal ArticleDOI
Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders
TL;DR: In this article, an autoencoder-based method was proposed for image anomaly detection in the medical domain, which relies on a re-designed training pipeline to handle high-resolution, complex images, and a robust way of computing an image abnormality score.
Journal ArticleDOI
Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey
TL;DR: In this article , a literature review on the application of GAN in ophthalmology image domains is presented to discuss important contributions and to identify potential future research directions, and a survey on studies using GAN published before June 2021 only is presented.
Journal ArticleDOI
Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.
TL;DR: In this paper, a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias is provided, highlighting the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.
Journal ArticleDOI
Deep learning models in medical image analysis.
TL;DR: In this paper , the authors present some solutions for this issue and discuss efforts needed to develop robust deep learning-based computer-aided diagnosis applications for better clinical workflow in endoscopy, radiology, pathology, and dentistry.
Journal ArticleDOI
Deep Learning for COVID-19 Diagnosis from CT Images
TL;DR: VGG19 architecture showed promising performance in the classification of COVID-19 cases and the cross-dataset experiments exposed the critical weakness of classifying images from heterogeneous data sources, compatible with a real-world scenario.
References
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