In-context Cross-Density Adaptation on Noisy Mammogram Abnormalities Detection
This work addresses a domain-specific challenge in medical imaging for mammogram analysis, but it is incremental as it builds on existing domain adaptation methods.
The paper tackled the problem of deep-learning models generalizing poorly across different breast densities in mammography images, especially with noisy labels, and found that using domain adaptation techniques with a proposed augmentation framework improved overall mAP by approximately 5%.
This paper investigates the impact of breast density distribution on the generalization performance of deep-learning models on mammography images using the VinDr-Mammo dataset. We explore the use of domain adaptation techniques, specifically Domain Adaptive Object Detection (DAOD) with the Noise Latent Transferability Exploration (NLTE) framework, to improve model performance across breast densities under noisy labeling circumstances. We propose a robust augmentation framework to bridge the domain gap between the source and target inside a dataset. Our results show that DAOD-based methods, along with the proposed augmentation framework, can improve the generalization performance of deep-learning models (+5% overall mAP improvement approximately in our experimental results compared to commonly used detection models). This paper highlights the importance of domain adaptation techniques in medical imaging, particularly in the context of breast density distribution, which is critical in mammography.