delahunt@uw.edu
Welcome! My work focuses on Machine Learning - (1) applications to global health care in LMICs (low and middle income countries), and (2) basic research. Below please find links to papers. (*) indicates lead author.
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Resumé
Machine learning: health care in LMICs
- (*) Metrics to guide development of machine learning algorithms for malaria diagnosis. In review 2023.
- Evaluation of an automated microscope using machine learning for the detection of malaria in travelers returned to the UK. Front Mal, 2023
- How good are synthetic medical images? An empirical study with lung ultrasound. MICCAI 2023
- Deep learning video classification of lung ultrasound features associated with pneumonia, CVPR 2023.
- Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on ML. Malaria J, 2022
- Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set. Malaria J 2021
- (*) Algorithms to predict moisture content of grain using relative humidity time-series. IEEE GHTC 2020
- (*) Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images. IEEE GHTC 2019
- Peru field trial of automated malaria diagnosis. Malaria Journal 2018
- Automated malaria diagnosis using CNNs. ICCV 2017
- (*) Automated microscopy and machine learning for malaria. IEEE GHTC 2015
- (*) Limitations of haemozoin-based diagnosis. Malaria J 2014
- (*) A toolkit for data-driven discovery of governing equations in high noise regimes. IEEE Access 2021
- (*) Built to Last: Functional and structural mechanisms in the moth olfactory network mitigate effects of neural injury. Brain Sciences, 2021
- (*) Predicting United States policy outcomes with Random Forests. INET 2020
- (*) Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy. 2019
- (*) Putting a bug in ML: The moth learns to read MNIST. Neural Networks 2019
- (*) Insect cyborgs: Bio-mimetic feature generators improve machine learning accuracy on limited data. NeurIPS 2019
- (*) Engineered for Function: The Power of Biologically Constrained Neural Networks for Neurosensory Integration. SIAM News July 2019
- (*) A moth brain learns to read MNIST. ICLR 2018
- (*) Biological Mechanisms for Learning. Frontiers Neuroscience 2018