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Found 2 papers, 1 papers with code
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images
Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time.
Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available.
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