Artificial Intelligence (AI) in Biomedical Image Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 1257
Special Issue Editors
Interests: machine learning; medical imaging; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; medical imaging; cardiovascular medicine
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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, endowing 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 algorithm is prone to small data permutations (e.g., misclassifying a panda as a gibbon with imperceptible noises). Addressing this 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 are 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 given to medical image processing. We invite authors who are interested in AI algorithms from both theoretical and practical perspectives and their applications 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
Guest Editors
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Keywords
- artificial intelligence
- machine learning
- image analysis
- computer-aided diagnosis
- visualization in biomedical imaging
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