Applications of deep learning for the analysis of medical data

Jang, Hyun‐Jong; Cho, Kyung-Ok

doi:10.1007/s12272-019-01162-9

Cited by 78 publications

(42 citation statements)

References 85 publications

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“…In this review, we focus on the application of deep learning to cancer. For more comprehensive information on deep learning, including its mathematical aspects, we recommend several recent reviews 2,10‐14 and a book 15…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in oncology

2020

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Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being applied in various areas of both basic and clinical cancer research. In this review, we describe numerous recent examples of the application of AI in oncology, including cases in which deep learning has efficiently solved problems that were previously thought to be unsolvable, and we address obstacles that must be overcome before such application can become more widespread. We also highlight resources and datasets that can help harness the power of AI for cancer research. The development of innovative approaches to and applications of AI will yield important insights in oncology in the coming decade.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence in oncology

2020

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“…The increasing amounts of data, as well as limited time and resources for processing and analysis, are not only a challenge in the field of biomolecular simulations. ML methods have gained enormous interest in recent years and are now applied in a wide range of research areas within biology, medicine, and health care ( 1 , 2 , 3 ) such as genomics ( 4 ), network biology ( 5 ), drug discovery ( 6 ), and medical imaging ( 7 , 8 ). In molecular simulations, such methods have, for example, eminently been used to enhance sampling by identifying CVs or the intrinsic dimensionality of biomolecular system in a data-driven manner ( 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ), as an interpolation or exploratory tool for generating new protein conformations ( 23 , 31 , 32 ), as well as providing a framework for learning biomolecular states and kinetics ( 11 , 33 , 34 ).…”

Section: Introductionmentioning

confidence: 99%

Molecular Insights from Conformational Ensembles via Machine Learning

Fleetwood

Kasimova

Westerlund

et al. 2020

Biophysical Journal

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Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size. Extracting important features from the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionality reduction tools. However, such methods are often criticized as resembling black boxes with limited human-interpretable insight. We use methods from supervised and unsupervised ML to efficiently create interpretable maps of important features from molecular simulations. We benchmark the performance of several methods, including neural networks, random forests, and principal component analysis, using a toy model with properties reminiscent of macromolecular behavior. We then analyze three diverse biological processes: conformational changes within the soluble protein calmodulin, ligand binding to a G protein-coupled receptor, and activation of an ion channel voltage-sensor domain, unraveling features critical for signal transduction, ligand binding, and voltage sensing. This work demonstrates the usefulness of ML in understanding biomolecular states and demystifying complex simulations.

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“…In contrast, feature extraction and model learning take place in a unified step in deep learning [9]. Deep learning-based approaches have become very successful in a wide range of biomedical analysis tasks [10], including the analysis of retinal fundus images [11], radiologic images [12], pathologic tissue images [13], electrocardiograms [14] and electroencephalograms [15]. However, deep learning-based methods generally require large annotated datasets compared to traditional machine learning [16].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of fully automated classification of whole slide images based on deep learning

Cho

Lee

Jang

2020

Korean J Physiol Pharmacol

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Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

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Applications of deep learning for the analysis of medical data

Cited by 78 publications

References 85 publications

Artificial intelligence in oncology

Artificial intelligence in oncology

Molecular Insights from Conformational Ensembles via Machine Learning

Feasibility of fully automated classification of whole slide images based on deep learning

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