Search Results for author:
Found 5 papers, 1 papers with code
Synergizing Data Imputation and Electronic Health Records for Advancing Prostate Cancer Research: Challenges, and Practical Applications
The objective is to exploit the inherent redundancies available within the integrated structured and unstructured data entries within EHRs in order to generate comprehensive and reliable medical databases, ready to be used in advanced research studies.
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.
