Mitigating annotation shift in cancer classification using single-image generative models

Marta Buetas Arcas; Richard Osuala; Karim Lekadir; Oliver Díaz

doi:10.1117/12.3025548

29 May 2024 Mitigating annotation shift in cancer classification using single-image generative models

Marta Buetas Arcas, Richard Osuala, Karim Lekadir, Oliver Díaz

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 1317421 (2024) https://doi.org/10.1117/12.3025548
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

Artificial Intelligence (AI) has emerged as a valuable tool for assisting radiologists in breast cancer detection and diagnosis. However, the success of AI applications in this domain is restricted by the quantity and quality of available data, posing challenges due to limited and costly data annotation procedures that often lead to annotation shifts. This study simulates, analyses and mitigates annotation shifts in cancer classification in the breast mammography domain. First, a high-accuracy cancer risk prediction model is developed, which effectively distinguishes benign from malignant lesions. Next, model performance is used to quantify the impact of annotation shift. We uncover a substantial impact of annotation shift on multiclass classification performance particularly for malignant lesions. We thus propose a training data augmentation approach based on single-image generative models for the affected class, requiring as few as four in-domain annotations to considerably mitigate annotation shift, while also addressing dataset imbalance. Lastly, we further increase performance by proposing and validating an ensemble architecture based on multiple models trained under different data augmentation regimes. Our study offers key insights into annotation shift in deep learning breast cancer classification and explores the potential of single-image generative models to overcome domain shift challenges.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Marta Buetas Arcas, Richard Osuala, Karim Lekadir, and Oliver Díaz "Mitigating annotation shift in cancer classification using single-image generative models", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 1317421 (29 May 2024); https://doi.org/10.1117/12.3025548

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KEYWORDS

Data modeling

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Image classification

Performance modeling

Statistical modeling

Artificial intelligence

Mammography

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