Deep Learning and Medical Diagnosis: A Review of Literature

By M. Bakator, Dragica Radosav. Multimodal Technologies and Interaction, 2018

462 citations recorded by the source corpus.

In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

Subfields: Artificial Intelligence, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/51c741acb1cf73eaed1c0e496ccc150bd8926939

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In Artificial Intelligence

In Neurology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A scoping review of transfer learning research on medical image analysis using ImageNet (2020, background): “Previous literature reviews focused on on the usage of non-TL based deep learning methods [6,7] and TLbased general machine learning methods for medical imaging [8].”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, background): “Surveys carried out by [254], [255] reveal that segmentation is the most sought out canonical task in medical image analysis.”
Learning to Synthesize Programs as Interpretable and Generalizable Policies (2021, unknown): “[99] Mihalj Bakator and Dragica Radosav.”