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.

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs.

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.

An automated methodology for whole-body, multimodality tracking of individual cancer lesions.

. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities.

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.