ProtoASNet: Dynamic Prototypes for Inherently Interpretable and
Uncertainty-Aware Aortic Stenosis Classification in Echocardiography

ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography

26 July 2023

Michael Y. Tsang

Armin Saadat

Purang Abolmaesumi

Papers citing "ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography"

6 / 6 papers shown

Title
Uncertainty Quantification for Machine Learning in Healthcare: A Survey L. J. L. Lopez Shaza Elsharief Dhiyaa Al Jorf Firas Darwish Congbo Ma Farah E. Shamout 52 0 0 04 May 2025
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography A. Gu Michael Y. Tsang H. Vaseli Teresa Tsang Purang Abolmaesumi 25 2 0 15 Sep 2024
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging Iury B. de A. Santos André C.P.L.F. de Carvalho MedIm 22 1 0 06 Apr 2024
Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis Zhe Huang Xiaowei Yu B. Wessler Michael C. Hughes 27 2 0 09 Mar 2024
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning Zhe Huang B. Wessler M. C. Hughes 14 3 0 25 May 2023
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis Benjamin Lambert Florence Forbes A. Tucholka Senan Doyle Harmonie Dehaene M. Dojat 24 76 0 05 Oct 2022