Deep Learning and Its Applications in Biomedicine

By Chensi Cao, Feng Liu, Hai Tan, Deshou Song, Wenjie Shu, Weizhong Li, Yiming Zhou, Xiaochen Bo, Zhi Xie. 2018

524 citations recorded by the source corpus.

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.

Subfields: Artificial Intelligence, Biophysics, Molecular Biology

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [102]27, covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [103, 104, 105, 106, 107, 108, 109, 110, 111, 1”
An Introductory Review of Deep Learning for Prediction Models With Big Data (2020, background): “…a historic summary with many detailed references in Schmidhuber (2015) and reviews about application domains, e.g., image analysis (Rawat and Wang, 2017; Shen et al., 2017), speech recognition (Yu and Li, 2017), natural language processing (Young et al., 2018), and biomedicine (Cao et al., 2018).”
LogBERT: Log Anomaly Detection via BERT (2021, background): “The anomaly in medical and public health domain is disease outbreak [11].”
Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, background): “The past few years have seen a wide range of AI applications to biomedicine researches, for example, medical image classification, genomic sequence analysis, classification and prediction, biomarkers, structural biology and chemistry, multiplatform data processing, drug discovery and repurposing [85”
Calibrating Deep Neural Networks using Focal Loss (2020, background): “Deep neural networks have dominated computer vision and machine learning in recent years, and this has led to their widespread deployment in real-world systems [2, 3, 11, 12, 38].”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, unknown): “S. Bhattacharya et al. environments, etc. DL can also generate data-driven features and manage high-dimensional data, whereas ML typically relies on hand-crafted features and only match low-dimensional data (Cao et al., 2018).”
State-of-the-art in artificial neural network applications: A survey (2018, background): “Interestingly DL techniques can be applying to explain genetic variants to identify pathogenic variants [101, 102].”
Deep Learning for Anomaly Detection: A Survey (2019, background): “Several studies have been conducted to understand the theoretical and practical applications of deep learning in medical and bio-informatics (Min et al. [2017], Cao et al. [2018a], Zhao et al. [2016], Khan and Yairi [2018]).”