Breast cancer risk prediction paper "OA-BreaCR" has been selected for oral presentation at MICCAI 2024 and won the “Young Scientist Award”!
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.
