Shuai Li

English

Cronfa at Swansea University

EDITORIALpublished: 01 March 2022doi: 10.3389/fdata.2022.863060Frontiers in Big Data | www.frontiersin.org 1 March 2022 | Volume 5 | Article 863060Edited and reviewed by:Thomas Hartung,Johns Hopkins University,United States*Correspondence:Yu-Dong Zhangyudongzhang@ieee.orgSpecialty section:This article was submitted toMedicine and Public Health,a section of the journalFrontiers in Big DataReceived: 26 January 2022Accepted: 28 January 2022Published: 01 March 2022Citation:Zhang Y-D, Li S, Cattani C andWang S-H (2022) Editorial: AdvancedDeep Learning Methods forBiomedical Information Analysis(ADLMBIA). Front. Big Data 5:863060.doi: 10.3389/fdata.2022.863060Editorial: Advanced Deep LearningMethods for Biomedical InformationAnalysis (ADLMBIA)Yu-Dong Zhang 1*, Shuai Li 2, Carlo Cattani 3 and Shui-Hua Wang 11 School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom, 2Department ofElectronics and Electrical Engineering, Swansea University, Swansea, United Kingdom, 3 Engineering School (DEIM),University of Tuscia, Viterbo, ItalyKeywords: deep learning, biomedical information analysis, magnetic resonance imaging, social media, omics,surface electromyography, optical character recognition, computed tomographyEditorial on the Research TopicAdvanced Deep Learning Methods for Biomedical Information Analysis (ADLMBIA)Due to numerous biomedical information sensing devices, such as Computed Tomography (CT),Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography(SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG,Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, andAtomic Force Microscopy, and so on, a large amount of biomedical information has been gatheredin recent years. However, developing advanced imaging methods and computational models forefficient data processing, analysis, and modeling from the collected data remains a challenge.Deep learning (DL) approaches have been rapidly developed in recent years, both in termsof methodologies and practical applications. DL techniques provide computational models ofmultiple processing layers to learn and represent data with multiple levels of abstraction. DL allowsthe capture of intricate structures of large-scale data implicitly and is ideally suited to some of thehardware architectures that are currently available.The purpose of this Research Topic is to provide a diverse but complementary setof contributions to demonstrate new developments and applications of deep learning andcomputational machine learning to solve problems in biomedical engineering.MRI and its related modalities are fundamental imaging tools in biomedical engineering. Themethod of Germuska et al. is based on the simultaneous acquisition of cerebral blood flow (CBF)and blood oxygen level-dependent (BOLD) weighted images during respiratorymodulation of bothoxygen and carbon dioxide. The authors present a machine learning implementation for the multi-parametric assessment of dual-calibrated fMRI data.Moreno López et al. evaluate two unsupervisedapproaches to denoise MRI in the complex image space using the raw information that k-spaceholds. The first method is based on Stein’s Unbiased Risk Estimator, while the second approach isbased on a blindspot network, limiting the network’s receptive field.Social media is now another critical tool that can provide information for biomedical analysis.Shah et al. use the advancement of natural language processing algorithms and large-scale dataanalysis. Their in-depth results show that the proposed method provides a viable solution in lesstime with the same accuracy compared to traditional methods.Omics are novel, comprehensive approaches for analyzing the genetic or molecular profilesof humans and other organisms. Hassanzadeh and Wang use an integrated deep belief networkto differentiate high-risk cancer patients from the low-risk ones in terms of overall survival.Their study analyzes RNA, miRNA, and methylation molecular data modalities from labeled andZhang et al. Editorial: Advanced Deep Learning Methods for Biomedical Information Analysis (ADLMBIA)unlabeled samples to predict cancer survival and subsequentlyprovide risk stratification.The surface electromyography (sEMG) assesses musclefunction by recording muscle activity above the muscle on theskin. In the paper of Shi et al., the knee flexion angle, hip flexionangle, ankle dorsiflexion angle, and sEMG signals of the sevenmuscles around the knee of three different data sets (walkingdata set, running data set, and walking and running mixed dataset) are used as input of the 1D CNN. The attention mechanismis added to the network to observe the dimension feature thatthe network pays more attention to, thereby increasing theinterpretability of the model.Optical character recognition (OCR) technology is a solutionfor automating data extraction from printed or written text froma scanned document or image file and then converting the textinto a machine-readable form to be used for data processing. Thepaper of Froese et al. builds a script that extracts real-time imagesfrom a medication pump and then processes them using OpticalCharacter Recognition to create digital text from the image.This text is then transferred to an ICM + real-time monitoringsoftware parallel with other retrieved physiological data.A computed tomography (CT) scan combines a series of X-ray images taken from different angles around the body and usescomputer processing to create cross-sectional images. The paperof Wang et al. builds a 12-layer convolutional neural network(12l-CNN) as the backbone network. Afterward, PatchShuffle isintroduced to integrate with 12l-CNN as a regularization termof the loss function. Their model is named PSCNN. Moreover,multiple-way data augmentation and Grad-CAM are employedto avoid overfitting and locating lung lesions.There is one review paper included in this Research Topic.Lan et al. introduce the origin, specific working principle,and development history of the generative adversarial network(GAN), various applications of GAN in digital image processing,Cycle-GAN, and its application in medical imaging analysis, aswell as the latest applications of GAN in medical informaticsand bioinformatics.The ultimate goal of this Research Topic is to promoteresearch and development of deep learning for multimodalbiomedical images by publishing high-quality research articles,reviews, or perspectives, among other article types, in this rapidlygrowing interdisciplinary field.AUTHOR CONTRIBUTIONSY-DZ was the guest editor of this Research Topic and wrote theeditorial. SL, CC, and S-HW were guest editors of this ResearchTopic. All GEs approved the submission of this editorial.ACKNOWLEDGMENTSWe thank the authors of the papers published in this ResearchTopic for their valuable contributions and the referees for theirrigorous review.Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.Publisher’s Note: All claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations, or those ofthe publisher, the editors and the reviewers. Any product that may be evaluated inthis article, or claim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.Copyright © 2022 Zhang, Li, Cattani and Wang. This is an open-access articledistributed under the terms of the Creative Commons Attribution License (CC BY).The use, distribution or reproduction in other forums is permitted, provided theoriginal author(s) and the copyright owner(s) are credited and that the originalpublication in this journal is cited, in accordance with accepted academic practice.No use, distribution or reproduction is permitted which does not comply with theseterms.Frontiers in Big Data | www.frontiersin.org 2 March 2022 | Volume 5 | Article 863060

Editorial: Advanced Deep Learning Methods for Biomedical Information Analysis (ADLMBIA)

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Editorial: Advanced Deep Learning Methods for Biomedical Information Analysis (ADLMBIA)

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