Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques
Oncotarget
|April 4, 2025
Summary
Deep learning models improve diagnostic accuracy in hepatobiliary imaging. Uncertainty quantification and novel networks like AHUNet enhance reliability for detecting cancer and precancerous lesions.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Deep learning significantly advances medical imaging analysis, especially in radiology.
- Hepatobiliary imaging benefits from AI for diagnosing oncological conditions and precancerous lesions.
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