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Found 6 papers, 3 papers with code
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Addressing catastrophic forgetting for medical domain expansion
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions
Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institution variation, such as multiple scanner types.
Towards Trainable Saliency Maps in Medical Imaging
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread acceptability is severely limited by inherent black-box decision making and unsafe failure modes.
Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging
Saliency maps have become a widely used method to make deep learning models more interpretable by providing post-hoc explanations of classifiers through identification of the most pertinent areas of the input medical image.
Assessing the validity of saliency maps for abnormality localization in medical imaging
Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network.
