1k citations · 1k across the 3 of their papers we have counts for
5 papers
Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance
Panagiotis Fytas, Ian Selby, Clemens Karner +14
Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment. However, commonly used report-derived…
SurvSurf: a partially monotonic neural network for first-hitting time prediction of intermittently observed discrete and continuous sequential events
Yichen Kelly Chen, Sören Dittmer, Kinga Bernatowicz +7
We propose a neural-network based survival model (SurvSurf) specifically designed for direct and simultaneous probabilistic prediction of the first hitting time of sequential event…
Parameter choices in HaarPSI for IQA with medical images
Clemens Karner, Janek Gröhl, Ian Selby +11
When developing machine learning models, image quality assessment (IQA) measures are a crucial component for the evaluation of obtained output images. However, commonly used full-r…
Recent Methodological Advances in Federated Learning for Healthcare
Fan Zhang, Daniel Kreuter, Yichen Chen +10
For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the util…
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Michael Roberts, Derek Driggs, Matthew Thorpe +13
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 (…