Artificial Intelligence in Biomedical Image Processing

Dear Colleagues,

Artificial intelligence (AI) has seen a dramatic resurgence in the past few years. With powerful computational resources and large datasets, AI is able to analyze, featuralize, predict, and generate data, giving great potential to benefit various fields, including biomedical image processing, immensely.

On the other hand, challenges also emerge in applying AI, and the deep-learning subtype in particular, to biomedical image processing. For instance, the availability of biomedical image datasets is usually limited due to the need for laborious manual labeling, privacy, and regulatory requirements. Additionally, it is hard to acquire data with different protocols, machines, and facilities, which are critical to verify the generalizability of AI algorithms. Second, the current AI is prone to small data permutations (e.g., miss-classifying a panda as a gibbon with imperceptible noises). Addressing it is necessary and significant, especially in medicine and healthcare, to reduce misdiagnosis and mistreatment. Last but not least, most AI models are still considered black boxes and hard to interpret, largely hindering their clinical usage.

This Special Issue focuses on the subject of artificial intelligence and its application in biomedical engineering, with special attention to medical image processing. We invite authors who are interested in AI algorithms from both theoretical and practical perspectives and their application in biomedical imaging, including but not limited to data acquisition, image reconstruction, image analysis and understanding, and computer-aided diagnosis.

Dr. Hongming Shan
Dr. Ruibin Feng
Dr. Zongwei Zhou
Guest Editors

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