1 citations · 2 across the 4 of their papers we have counts for
5 papers
FloatSOM: GPU-Accelerated, Distributed, Topology-Flexible Self-Organizing Maps
Tony Xu, Sarah Klamt, Katherine Turner +3
GPU-accelerated Self-Organizing Map (SOM) implementations are among the most competitive options for large-scale SOM analysis, but growing dataset sizes increasingly challenge thei…
Resource and data efficient self supervised learning
Ozan Ciga, Tony Xu, Anne L. Martel
We investigate the utility of pretraining by contrastive self supervised learning on both natural-scene and medical imaging datasets when the unlabeled dataset size is small, or wh…
Overcoming the limitations of patch-based learning to detect cancer in whole slide images
Ozan Ciga, Tony Xu, Sharon Nofech-Mozes +3
Whole slide images (WSIs) pose unique challenges when training deep learning models. They are very large which makes it necessary to break each image down into smaller patches for…
Self supervised contrastive learning for digital histopathology
Ozan Ciga, Tony Xu, Anne L. Martel
Unsupervised learning has been a long-standing goal of machine learning and is especially important for medical image analysis, where the learning can compensate for the scarcity o…
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
Ademola Oladosu, Tony Xu, Philip Ekfeldt +5
This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class,…