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Found 4 papers, 1 papers with code
Augment like there's no tomorrow: Consistently performing neural networks for medical imaging
How to recognize models suffering from this fragility, and how to design robust models are the main obstacles to clinical adoption.
Classification of datasets with imputed missing values: does imputation quality matter?
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
Spectral decoupling allows training transferable neural networks in medical imaging
We show that spectral decoupling allows training neural networks on datasets with strong spurious correlations and increases networks' robustness for data distribution shifts.
Improving Prostate Cancer Detection with Breast Histopathology Images
We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training.
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