Advances in Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging—Volume 2

Dear Colleagues,

Cancer ranks the second most common cause of death in many countries, following cardiovascular diseases [1]. Therefore, early detection and diagnosis are crucial for improving the 5-year survival rate [2]. Screening examination plays an essential role in diagnosing diseases [3], requiring physicians to interpret many medical images. However, human interpretation has many limitations, including inaccuracy, distraction, and fatigue, which may lead to false positives and false negatives that lead to improper treatment. Therefore, a computer-aided diagnosis (CAD) system is needed as a second opinion system to diagnose ambiguous cases to solve these limitations. Computer-aided diagnostic (CAD) systems use classical image processing, computer vision, machine learning, and deep learning methods for image analysis. Using image classification or segmentation algorithms, they find a region of interest (ROI) pointing to a specific location within the given image or the outcome of interest in the form of a label pointing to a diagnosis or prognosis. This Special Issue focuses on advanced CAD methods that use artificial intelligence (AI) approaches in various imaging modalities, such as X-ray, computed tomography (CT), positron emission tomography (PET), ultrasound, MRI, immunohistochemistry, and hematoxylin and eosin (H&E) whole slide images (WSIs), toward the end diagnosis or prognosis.

[1] Huang, X.; Xiao, R.; Pan, S.; Yang, X.; Yuan, W.; Tu, Z.; et al. Uncovering the roles of long non-coding RNAS in cancer stem cells. J. Hematol. Oncol. 2017, 10, 62. doi: 10.1186/s13045-017-0428-9.

[2] Mohaghegh, P.; Rockall, A.G. Imaging strategy for early ovarian cancer: Characterization of adnexal masses with conventional and advanced imaging techniques. Radiographics 2012, 32, 1751–1773.

[3] Sarigoz, T.; Ertan, T.; Topuz, O.; Sevim, Y.; Cihan, Y. Role of digital infrared thermal imaging in the diagnosis of breast mass: A pilot study. Infrared Phys. Technol. 2018, 91, 214–219.

Dr. Farhan Akram
Guest Editor

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• cancer diagnosis
• medical images
• electronic health records
• machine learning
• deep learning
• artificial intelligence
• explainable AI models
• multi-modal analysis
• federated learning
• CAD systems