Charles B. Delahunt

发表

J. Nathan Kutz, Charles B. Delahunt, 2018, ICLR.

Computer-Automated Malaria Diagnosis and Quantitation Using Convolutional Neural Networks

Peter Chiodini, David Bell, Mayoore S. Jaiswal, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images

Jane Carter, Mayoore S. Jaiswal, Samantha Janko, 2019, 2019 IEEE Global Humanitarian Technology Conference (GHTC).

Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data pdf

J. Nathan Kutz, Charles B. Delahunt, 2018, ArXiv.

Putting a bug in ML: The moth olfactory network learns to read MNIST

J. Nathan Kutz, Charles B. Delahunt, J. Kutz, 2018, Neural Networks.

Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, With Applications to Neural Nets

J. Nathan Kutz, Jeffrey A. Riffell, Charles B. Delahunt, 2018, Front. Comput. Neurosci..

Insect cyborgs: Bio-mimetic feature generators improve machine learning accuracy on limited data

Charles B. Delahunt, 2018 .

Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy pdf

J. Nathan Kutz, Charles B. Delahunt, Courosh Mehanian, 2019, ArXiv.

Automated microscopy and machine learning for expert-level malaria field diagnosis

Shawn K. McGuire, Charles B. Delahunt, Courosh Mehanian, 2015, 2015 IEEE Global Humanitarian Technology Conference (GHTC).

Insect Cyborgs: Bio-mimetic Feature Generators Improve ML Accuracy on Limited Data

J. Nathan Kutz, Charles B. Delahunt, J. Kutz, 2019 .

The Challenges and Prospects of Microscopy and Biophotonics in Low Resource Settings

Charles B. Delahunt, Courosh Mehanian, Mayoore S. Jaiswal, 2018 .

PySINDy: A comprehensive Python package for robust sparse system identification pdf

Steven L. Brunton, J. Nathan Kutz, Charles B. Delahunt, 2021, J. Open Source Softw..

A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes pdf

J. Nathan Kutz, Charles B. Delahunt, J. Kutz, 2021, IEEE Access.

Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning

Shawn K. McGuire, Charles B. Delahunt, R. Price, 2022, Malaria journal.

A paper microfluidic cartridge for automated staining of malaria parasites with an optically transparent microscopy window.

Charles B. Delahunt, K. Nichols, M. Horning, 2014, Lab on a chip.

Automated microscopy for routine malaria diagnosis: a field comparison on Giemsa-stained blood films in Peru

Mayoore S. Jaiswal, Shawn K. McGuire, Charles B. Delahunt, 2018, Malaria Journal.

Use case-focused metrics to evaluate machine learning for diseases involving parasite loads

Charles B. Delahunt, M. Horning, Noni Gachuhi, 2022, ArXiv.

Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set

Mayoore S. Jaiswal, Charles B. Delahunt, Benjamin K. Wilson, 2021, Malaria journal.

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images

Mayoore S. Jaiswal, Charles B. Delahunt, Benjamin K. Wilson, 2019, 2019 IEEE Global Humanitarian Technology Conference (GHTC).

Automated microscopy for routine malaria diagnosis: a field comparison on Giemsa-stained blood films in Peru

Mayoore S. Jaiswal, Shawn K. McGuire, Charles B. Delahunt, 2018, Malaria Journal.

Built to Last: Functional and Structural Mechanisms in the Moth Olfactory Network Mitigate Effects of Neural Injury

J. Nathan Kutz, Charles B. Delahunt, Pedro D. Maia, 2018, Brain sciences.

Deep Learning Video Classification of Lung Ultrasound Features Associated with Pneumonia

Charles B. Delahunt, Sourabh Kulhare, M. Horning, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Evaluation of an automated microscope using machine learning for the detection of malaria in travelers returned to the UK

Charles B. Delahunt, M. Gatton, P. Chiodini, 2023, Frontiers in Malaria.

Metrics to guide development of machine learning algorithms for malaria diagnosis

Charles B. Delahunt, M. Horning, Noni Gachuhi, 2022, 2209.06947.

Smart as a Bug: A Computational Model of Learning in the Moth Olfactory Network, with Applications to Neural Nets

Charles B. Delahunt, Charles Boise Delahunt, 2018 .

Predicting United States policy outcomes with Random Forests pdf

Shawn McGuire, Charles B. Delahunt, Charles Delahunt, 2020, Institute for New Economic Thinking Working Paper Series.

Limitations of haemozoin-based diagnosis of Plasmodium falciparum using dark-field microscopy

Charles B. Delahunt, Benjamin K. Wilson, J. Proctor, 2014, Malaria Journal.