Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review
Mingquan Lin, Jacob Wynne, Yang Lei, Tonghe Wang, Walter J. Curran,, Tian Liu, Xiaofeng Yang
TL;DR
This review paper discusses recent AI methods for analyzing tumor subregions in medical imaging, highlighting their applications, strategies, challenges, and future potential in cancer diagnosis and treatment.
Contribution
It provides a comprehensive categorization and summary of supervised and unsupervised AI techniques for tumor subregion analysis in medical imaging.
Findings
Summarizes recent AI methods and their applications in tumor subregion analysis.
Highlights key achievements and contributions in supervised and unsupervised strategies.
Discusses challenges and future directions in AI-based tumor analysis.
Abstract
Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential AI applications in tumor subregion analysis are discussed.
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Taxonomy
TopicsRadiomics and Machine Learning in Medical Imaging · AI in cancer detection · Brain Tumor Detection and Classification