Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques DOI Open Access
Yashbir Singh, Jesper B. Andersen, Quincy A. Hathaway

et al.

Oncotarget, Journal Year: 2025, Volume and Issue: 16(1), P. 249 - 255

Published: Jan. 20, 2025

Recent advances in deep learning models have transformed medical imaging analysis, particularly radiology. This editorial outlines how uncertainty quantification through embedding-based approaches enhances diagnostic accuracy and reliability hepatobiliary imaging, with a specific focus on oncological conditions early detection of precancerous lesions. We explore modern architectures like the Anisotropic Hybrid Network (AHUNet), which leverages both 2D 3D volumetric data innovative convolutional approaches. consider implications for quality assurance radiological practice discuss recent clinical applications.

Language: Английский

Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques DOI Open Access
Yashbir Singh, Jesper B. Andersen, Quincy A. Hathaway

et al.

Oncotarget, Journal Year: 2025, Volume and Issue: 16(1), P. 249 - 255

Published: Jan. 20, 2025

Recent advances in deep learning models have transformed medical imaging analysis, particularly radiology. This editorial outlines how uncertainty quantification through embedding-based approaches enhances diagnostic accuracy and reliability hepatobiliary imaging, with a specific focus on oncological conditions early detection of precancerous lesions. We explore modern architectures like the Anisotropic Hybrid Network (AHUNet), which leverages both 2D 3D volumetric data innovative convolutional approaches. consider implications for quality assurance radiological practice discuss recent clinical applications.

Language: Английский

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