5 papers
Embracing Annotation Efficient Learning (AEL) for Digital Pathology and Natural Images
Eu Wern Teh
Jitendra Malik once said, "Supervision is the opium of the AI researcher". Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively. In tod…
Understanding the impact of image and input resolution on deep digital pathology patch classifiers
Eu Wern Teh, Graham W. Taylor
We consider annotation efficient learning in Digital Pathology (DP), where expert annotations are expensive and thus scarce. We explore the impact of image and input resolution on…
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images
Eu Wern Teh, Graham W. Taylor
A critical challenge of training deep learning models in the Digital Pathology (DP) domain is the high annotation cost by medical experts. One way to tackle this issue is via trans…
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
Eu Wern Teh, Terrance DeVries, Graham W. Taylor
We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several en…
Learning with less data via Weakly Labeled Patch Classification in Digital Pathology
Eu Wern Teh, Graham W. Taylor
In Digital Pathology (DP), labeled data is generally very scarce due to the requirement that medical experts provide annotations. We address this issue by learning transferable fea…