Innovative Computer Vision Solutions for Medical Image Analysis
A special issue of Computers (ISSN 2073-431X).
Deadline for manuscript submissions: 31 October 2025 | Viewed by 86
Special Issue Editor
Special Issue Information
Dear Colleagues,
Medical image analysis encompasses a range of computational methodologies that leverage AI-driven computer vision techniques to extract clinically meaningful information from diagnostic images, functioning as a decision-support tool throughout the entire healthcare continuum. However, the exponential growth of multimodal imaging data—from high-resolution scans (e.g., CT and MRI) to dynamic sequences (e.g., ultrasonic imaging), and functional imaging (e.g., positron emission tomography)—presents unprecedented computational and analytical challenges for both researchers and clinicians. Thus, data processing, analysis, and interpretation represent some of the most critical yet formidable challenges in medical image analysis.
In most cases, bias arises from heterogeneous data distributions, class imbalance, and missing values in medical data, posing a major challenge to the development of robust diagnostic models. This Special Issue aims to enhance the generalizability and interpretability of AI-assisted diagnostic systems by developing novel computer vision algorithms tailored to multimodal medical imaging. Key approaches include, but are not limited to, data distribution calibration, synthetic sample generation, and cross-modal feature fusion, with particular emphasis on adapting these solutions to personalized clinical settings for improved practical utility.
Dr. Fengjun Zhao
Guest Editor
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Keywords
- medical image analysis
- computer vision
- generalizability
- interpretability
- class imbalance
- image generation
- feature fusion
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