Deep Learning in Medical Image Analysis

By Dinggang Shen, Guorong Wu, Heung-Il Suk. 2017

2,935 citations recorded by the source corpus.

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Deep learning has been used to classify lesions and nodules; localize organs, regions, landmarks and lesions; segment organs, organ substructures and lesions; retrieve images based on content; generate and enhance images; and combine images with clinical reports [40,41].”
A survey on deep learning in medical image analysis (2017, background): “One dedicated review on application of deep learning to medical image analysis was published by Shen et al. (2017).”
Deep Learning in Mining Biological Data (2020, methodology): “ation with gradient based algorithm is used to obtain the optimal weights. But this suffers from poor local minima problem which is overcome by pretraining the network with greedy layer-wise learning [30]. Despite the pretraining stage and vanishing error problem [31], DA is a popular data compressi”
Artificial intelligence in oncology (2020, background): “24,25 Whole-slide imaging is becoming routine in developed countries, which has resulted in the accumulation of digital pathology images and allowed the application of deep learning to pathological diagnosis.”
Artificial intelligence in radiology (2018, positive): “Deep learning can thus automatically quantify phenotypic characteristics of human tissues 32 , promising substantial improvements in diagnosis and clinical care.”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “A recent survey by Litjens et al. [213] presents a collective picture of the prevalence and applications of deep learning models within the community as does a fairly rigorous treatise of the same by Shen et al. [214].”
MILD‐Net: Minimal information loss dilated network for gland instance segmentation in colon histology images (2018, neutral): “Recently, et al., 2017; Shen et al., 2017; LeCun et al., 2015).”
Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model (2017, neutral): “+ < + < + − D x p m D x p m D x n ( , ) ( , ) ( , ) (7) i i i i i i 1 2”
Plant leaf disease classification using EfficientNet deep learning model (2021, neutral): “The use of deep learning methods in the diagnosis and classification of diseases from medical images is quite common (Shen et al., 2017).”