42 citations · 116 across the 13 of their papers we have counts for
14 papers
Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning
Jeffrey Gu, Serena Yeung
Creating representations of shapes that are invari-ant to isometric or almost-isometric transforma-tions has long been an area of interest in shape anal-ysis, since enforcing invar…
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images
Joy Hsu, Wah Chiu, Serena Yeung
In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge. Labelled…
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision
Zhenzhen Weng, Mehmet Giray Ogut, Shai Limonchik +1
Instance segmentation is an active topic in computer vision that is usually solved by using supervised learning approaches over very large datasets composed of object level masks.…
Using Computer Vision to Automate Hand Detection and Tracking of Surgeon Movements in Videos of Open Surgery
Michael Zhang, Xiaotian Cheng, Daniel Copeland +4
Open, or non-laparoscopic surgery, represents the vast majority of all operating room procedures, but few tools exist to objectively evaluate these techniques at scale. Current eff…
Personalized Federated Learning with First Order Model Optimization
Michael Zhang, Karan Sapra, Sanja Fidler +2
While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients. Her…
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
Joy Hsu, Jeffrey Gu, Gong-Her Wu +2
We consider the task of representation learning for unsupervised segmentation of 3D voxel-grid biomedical images. We show that models that capture implicit hierarchical relationshi…