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Updated: Jan 30, 2026
04:48
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
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[Medical Application of Artificial Intelligence/Deep Learning]
Jun Miyake1, Hironori Ohigashi, Hirohiko Niioka
1Global Center for Medical Engineering and Informatics, Osaka University.
Brain and Nerve = Shinkei Kenkyu No Shinpo
|January 11, 2019
Summary
Deep learning, a form of artificial intelligence, shows promise in medical imaging and diagnosis. This technology analyzes complex biological systems, aiding in areas like cancer recurrence prediction.
Area of Science:
- Medical Artificial Intelligence
- Computational Biology
Background:
- Deep learning is a subset of artificial intelligence with significant applications in medicine.
- Its potential is recognized in interpreting radiographic images, pathological diagnosis, gene analysis, and predicting cancer recurrence.
Purpose of the Study:
- To summarize the concept of deep learning in the context of medical applications.
- To highlight deep learning's role in analyzing complex biological systems.
Main Methods:
- Conceptual summary of deep learning.
- Application of deep learning principles to analyze human biological systems.
- Categorization of obstacles within these systems.
Main Results:
- Deep learning offers a novel approach to analyzing complex biological systems, from molecular to functional levels.
- The technology aids in categorizing diagnostic and prognostic obstacles.
- Deep learning models demonstrate an ability to approximate human physician cognitive processes.
Conclusions:
- Deep learning presents a powerful tool for advancing medical diagnostics and prognostics.
- Its application in analyzing biological complexity can enhance understanding and prediction in diseases like cancer.
- The technology shows potential to augment clinical decision-making by mimicking physician cognition.
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