Dr. Tong Li Profile

Dr. Tong Li

at Univ of Sydney

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Proceedings Article | 29 March 2024 Presentation + Paper

CNN-based transfer learning with 10-fold cross-validation: a novel approach for customized education of mammography training

Xuetong Tao, Ziba Gandomkar, Tong Li, Jessica Yi, Patrick Brennan, Warren Reed

Proceedings Volume 12929, 1292904 (2024) https://doi.org/10.1117/12.3006659

KEYWORDS: Mammography, Education and training, Performance modeling, Cross validation, Diagnostics, Breast cancer, Radiology, Data modeling, Breast

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Early detection of breast cancer through screening mammography is crucial. However, the interpretation of mammograms is prone to high error rates, and radiologists often exhibit common errors specific to their practice regions. It is essential to identify prevalent errors and offer tailored mammography training to address these region-specific challenges. This study investigated the feasibility of leveraging Convolutional Neural Networks (CNN) with transfer learning to identify areas in screening mammograms that may contribute to a high proportion of false positive diagnoses by radiologists from the same geographical region. We collected mammography test sets evaluated by a cohort of Australian radiologists and segmented error-related patches based on their assessments. Each patch was labeled as “easy” or “difficult”, and subsequently, we proposed a patch-wise ResNet model to predict the difficulty level of each patch. Specifically, we employed the pre-trained ResNet-18, ResNet-50, and ResNet-101 as feature extractors. During training, we modified and fine-tuned the fully connected layers for our target task while keeping the convolutional layers frozen. The model’s performance was evaluated using 10-fold cross-validation, and the transferred ResNet-50 obtained the highest performance, achieving Receiver Operating Characteristics Area Under the Curve (AUC) values of 0.975 (±0.011) on the validation sets. In conclusion, our study demonstrated the feasibility of employing CNN-based transfer learning to identify the prevalent errors in specific radiology communities. This approach shows promise in automating the customization of mammography training materials to mitigate errors among radiologists in a region.

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