Deep Learning for Computer-Aided Diagnosis in Biomedical Imaging

Dear Colleagues,

Deep learning has led to dramatic advances in the analysis of images and video and has demonstrated the potential to transform computer-aided diagnosis in biomedical imaging. Innovations in algorithm and software development and availability of larger annotated biomedical imaging datasets are driving improvements in automated classification, localization, retrieval, and segmentation of molecules, cells, lesions, nodules, tumors, organs and other structures of interest. Deep neural networks have also been employed for medical image generation and enhancement, and integration of images and biomedical data of other modalities. However, applications of deep learning to computer-assisted diagnosis in biomedical imaging also pose important challenges, such as learning from small, imbalanced, and noisy data, estimating model uncertainty, evaluating and interpreting models, limiting computing requirements, and designing and building interfaces between algorithms and clinicians. This Special Issue will focus on recent advances, prospects and challenges in deep learning applications to computer-aided biomedical imaging.

Dr. Alexandr Kalinin
Guest Editor

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