Longitudinal interpretability of deep learning based breast cancer risk prediction.

Deep-learning-based models have achieved state-of-the-art breast cancer risk (BCR) prediction performance. However, these models are highly complex, and the underlying mechanisms of BCR prediction are not fully understood. Key questions include whether these models can detect breast morphologic changes that lead to cancer.

Models using comprehensive, lesion-level, longitudinal [Ga]Ga-DOTA-TATE PET-derived features lead to superior outcome prediction in neuroendocrine tumor patients treated with [Lu]Lu-DOTA-TATE.

Purpose: Somatostatin receptor (SSTR) imaging features are predictive of treatment outcome for neuroendocrine tumor (NET) patients receiving peptide receptor radionuclide therapy (PRRT). However, comprehensive (all metastatic lesions), longitudinal (temporal variation), and lesion-level measured features have never been explored. Such features allow for capturing the heterogeneity in disease response to treatment.

Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout.

. Deep Learning models are often susceptible to failures after deployment. Knowing when your model is producing inadequate predictions is crucial.