249 citations · 782 across the 6 of their papers we have counts for
6 papers
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda +8
Antibodies have become an important class of therapeutic agents to treat human diseases. To accelerate therapeutic antibody discovery, computational methods, especially machine lea…
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy +10
Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investiga…
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Avinash Varadarajan, Pinal Bavishi, Paisan Raumviboonsuk +15
Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become incre…
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch +26
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manua…
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer +6
Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer…
Deep learning for predicting refractive error from retinal fundus images
Avinash V. Varadarajan, Ryan Poplin, Katy Blumer +7
Refractive error, one of the leading cause of visual impairment, can be corrected by simple interventions like prescribing eyeglasses. We trained a deep learning algorithm to predi…