Breast cancer risk prediction paper "OA-BreaCR" has been selected for oral presentation at MICCAI 2024

Precision and explainability are vital in breast cancer risk assessment for developing personalized screening and prevention strategies. We introduce OA-BreaCR, a novel method that precisely evaluates both the likelihood and timing of future breast cancer occurrence using sequential mammograms. To enhance Precision: By employing ordinal learning, OA-BreaCR models temporal information based on the 'time-to-future-event' ordering among patients, improving the precision of time predictions. To improve Explainability: The method utilizes an attention alignment mechanism to effectively track high-risk breast tissue changes over time, enhancing the model's interpretability.