# name
Mitigating annotation shift in cancer classification using single image generative models

# metadata.dc.description.abstract[].value
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. All code used for
this study is made publicly available at https://github.com/MartaBuetas/EnhancingBreastCancerDiagnosis.

# metadata.dc.relation.isformatof[].value
Versió postprint de la comunicació publicada a: https://doi.org/10.1117/12.3025548

# metadata.dc.relation.ispartof[].value
Comunicació a: Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 1317421 (29 May 2024)

# metadata.dc.source[].value
Comunicacions a congressos  (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Mitigating annotation shift in cancer classification using single image generative models