AI for Advanced Biomedical Applications

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Artificial Intelligence (AI) is advancing biomedical science in many ways, from improving image-based diagnostics to engineering strategies for improving movement related to injury, birth defects, or neurological or cardiovascular disease, to predicting behavior and nerve responses to stimuli. BME Frontiers is assembling a special issue highlighting the latest applications of AI to biomedical science and invites you to submit your research for consideration.

The focus will be primarily on the application of AI to biomedical imaging, however, studies related to other biomedical applications of AI are welcome. If your research applies AI to any of these areas, we encourage you to submit: 

  • Learning-based algorithms for biological or medical imaging, including disease diagnosis and prognosis, improving image clarity or resolution, and enabling cross-modality comparison
  • Automated approaches, such as computer vision, to localizing and tracking moving targets
  • Image-guided therapy, including planning for and execution of surgical procedures and during surgical interventions
  • Image-omics and radiomics in disease diagnosis and therapy, including quantitative approaches to molecular diagnostic and therapeutic imaging
  • Discovery-based applications of AI in biomedicine 
  • Fusion of machine learning and domain knowledge, including feature extraction and computational modeling from biomedical images and annotation-efficient learning from biomedical imaging
  • Biomedical image synthesis and editing
  • Detection and segmentation from biomedical images

    Guest Editors

    Professor Rama Chellappa, Johns Hopkins University
    Professor S. Kevin Zhou, University of Science and Technology of China
    Professor Jeremy Walston, Johns Hopkins School of Medicine

      Table of Contents

      As articles within the special issue are published they will appear below.

      Simulated MRI Artifacts: Testing Machine Learning Failure Modes

      Nicholas C. Wang, Douglas C. Noll, Ashok Srinivasan, Johann Gagnon-Bartsch, Michelle M. Kim, Arvind Rao

      BME Frontiers, vol. 2022, Article ID 9807590, 16 pages, 2022

      Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation

      Joshua K. Peeples, Julie F. Jameson, Nisha M. Kotta, Jonathan M. Grasman, Whitney L. Stoppel, Alina Zare

      BME Frontiers, vol. 2022, Article ID 9854084, 14 pages, 2022

      A Low-Cost High-Performance Data Augmentation for Deep Learning-Based Skin Lesion Classification

      Shuwei Shen, Mengjuan Xu, Fan Zhang, Pengfei Shao, Honghong Liu, Liang Xu, Chi Zhang, Peng Liu, Zhihong Zhang, Peng Yao, Ronald X. Xu

      BME Frontiers, vol. 2022, Article ID 9765307, 12 pages, 2022

      Impedance Imaging of Cells and Tissues: Design and Applications

      Raziyeh Bounik, Fernando Cardes, Hasan Ulusan, Mario M. Modena, Andreas Hierlemann

      BME Frontiers, vol. 2022, Article ID 9857485, 21 pages, 2022

      A Deep Learning Approach for Detecting Colorectal Cancer via Raman Spectra

      Zheng Cao, Xiang Pan, Hongyun Yu, Shiyuan Hua, Da Wang, Danny Z. Chen, Min Zhou, Jian Wu

      BME Frontiers, vol. 2022, Article ID 9872028, 10 pages, 2022

      Automatic Detection of Atrial Fibrillation from Single-Lead ECG Using Deep Learning of the Cardiac Cycle

      Alina Dubatovka, Joachim M. Buhmann

      BME Frontiers, vol. 2022, Article ID 9813062, 12 pages, 2022

      Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma

      Jifei Wang, Dasheng Wu, Meili Sun, Zhenpeng Peng, Yingyu Lin, Hongxin Lin, Jiazhao Chen, Tingyu Long, Zi-Ping Li, Chuanmiao Xie, Bingsheng Huang, Shi-Ting Feng

      BME Frontiers, vol. 2022, Article ID 9793716, 12 pages, 2022

      Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders

      Wei Xiong, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, Jiebo Luo

      BME Frontiers, vol. 2022, Article ID 9763284, 10 pages, 2022

      Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution

      Peiting You, Xiang Li, Fan Zhang, Quanzheng Li

      BME Frontiers, vol. 2022, Article ID 9814824, 11 pages, 2022

      Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

      Hailing Liu, Yu Zhao, Fan Yang, Xiaoying Lou, Feng Wu, Hang Li, Xiaohan Xing, Tingying Peng, Bjoern Menze, Junzhou Huang, Shujun Zhang, Anjia Han, Jianhua Yao, Xinjuan Fan

      BME Frontiers, vol. 2022, Article ID 9860179, 12 pages, 2022

      Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts

      Alex Ling Yu Hung, Edward Chen, John Galeotti

      BME Frontiers, vol. 2022, Article ID 9837076, 15 pages, 2022

      A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

      Lingyi Zhao, Muyinatu A. Lediju Bell

      BME Frontiers, vol. 2022, Article ID 9780173, 17 pages, 2022

      Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

      Angela Zhang, Amil Khan, Saisidharth Majeti, Judy Pham, Christopher Nguyen, Peter Tran, Vikram Iyer, Ashutosh Shelat, Jefferson Chen, B. S. Manjunath

      BME Frontiers, vol. 2022, Article ID 9783128, 13 pages, 2022