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Found 7 papers, 3 papers with code
The curious case of the test set AUROC
Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace.
Recent Methodological Advances in Federated Learning for Healthcare
We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare.
Reinterpreting survival analysis in the universal approximator age
We aim to provide the tools needed to fully harness the potential of survival analysis in deep learning.
Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data
Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites.
Navigating the challenges in creating complex data systems: a development philosophy
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.
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.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.
