6 citations · 18 across the 9 of their papers we have counts for
13 papers
Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound
Paul F. R. Wilson, Mahdi Gilany, Amoon Jamzad +5
Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasou…
Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography
Fatemeh Taheri Dezaki, Christina Luong, Tom Ginsberg +4
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergenci…
U-LanD: Uncertainty-Driven Video Landmark Detection
Mohammad H. Jafari, Christina Luong, Michael Tsang +5
This paper presents U-LanD, a framework for joint detection of key frames and landmarks in videos. We tackle a specifically challenging problem, where training labels are noisy and…
Reciprocal Landmark Detection and Tracking with Extremely Few Annotations
Jianzhe Lin, Ghazal Sahebzamani, Christina Luong +4
Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic locali…
PEP: Parameter Ensembling by Perturbation
Alireza Mehrtash, Purang Abolmaesumi, Polina Golland +3
Ensembling is now recognized as an effective approach for increasing the predictive performance and calibration of deep networks. We introduce a new approach, Parameter Ensembling…
Variational Learning with Disentanglement-PyTorch
Amir H. Abdi, Purang Abolmaesumi, Sidney Fels
Unsupervised learning of disentangled representations is an open problem in machine learning. The Disentanglement-PyTorch library is developed to facilitate research, implementatio…