4 papers
Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics
Ang Nan Gu, Michael Tsang, Hooman Vaseli +2
Computer-aided diagnosis systems must make critical decisions from medical images that are often noisy, ambiguous, or conflicting, yet today's models are trained on overly simplist…
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography
Ang Nan Gu, Michael Tsang, Hooman Vaseli +2
The fundamental problem with ultrasound-guided diagnosis is that the acquired images are often 2-D cross-sections of a 3-D anatomy, potentially missing important anatomical details…
ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography
Hooman Vaseli, Ang Nan Gu, S. Neda Ahmadi Amiri +6
Aortic stenosis (AS) is a common heart valve disease that requires accurate and timely diagnosis for appropriate treatment. Most current automatic AS severity detection methods rel…
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…