Poster + Paper
2 April 2024 Supervised and semisupervised methods of nematode images classification for drug discovery
Author Affiliations +
Conference Poster
Abstract
We use computer vision to accelerate the discovery of antiparasitic drug candidates. We trained supervised and semi-supervised deep learning models for identifying images in which the natural product extracts being screened as drug candidates have effectively impacted nematode development. We have developed a novel dataset comprising 12,800 images, consisting of 4,640 labeled and 8,160 unlabeled nematode images. We report the performance of a variety of deep neural networks and loss functions in this application and show that DenseNet provides an accuracy of 86%. We also extended the approach to a semi-supervised learning methodology, using high-confidence pseudo-labels from unlabeled data to augment the training set iteratively. This semi-supervised method allows for the use of unlabeled data and contributes to enhanced test classification performance.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Lyuyang Wang, Sommer Chou, Gerry Wright, Lesley MacNeil, and Mehdi Moradi "Supervised and semisupervised methods of nematode images classification for drug discovery", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129301R (2 April 2024); https://doi.org/10.1117/12.3006583
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Drug discovery

Image classification

Artificial intelligence

Education and training

Neural networks

Back to Top