30 citations · 62 across the 5 of their papers we have counts for
5 papers
Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean
Balagopal Unnikrishnan, Cuong Nguyen, Shafa Balaram +3
Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets.…
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations
Navid Alemi Koohbanani, Balagopal Unnikrishnan, Syed Ali Khurram +2
While high-resolution pathology images lend themselves well to `data hungry' deep learning algorithms, obtaining exhaustive annotations on these images is a major challenge. In thi…
Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments
Balagopal Unnikrishnan, Pranshu Ranjan Singh, Xulei Yang +1
Automated heart sounds classification is a much-required diagnostic tool in the view of increasing incidences of heart related diseases worldwide. In this study, we conduct a compr…
CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images
Jin Chao, Ahmad Al Badawi, Balagopal Unnikrishnan +9
Convolutional neural networks (CNNs) have enabled significant performance leaps in medical image classification tasks. However, translating neural network models for clinical appli…
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo +7
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require…