Neural network training with highly incomplete medical datasets (Conference Presentation)

Yu-Wei Chang

doi:10.1117/12.2655654

4 October 2022 Neural network training with highly incomplete medical datasets (Conference Presentation)

Yu-Wei Chang

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040L (2022) https://doi.org/10.1117/12.2655654
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer's disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.

Conference Presentation

Citation Download Citation

Yu-Wei Chang "Neural network training with highly incomplete medical datasets (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040L (4 October 2022); https://doi.org/10.1117/12.2655654

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