Supervised and semisupervised methods of nematode images classification for drug discovery

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.