bibtext: @InProceedings{ Gu_Reliable_MICCAI2024,
                   author = { Gu, Ang Nan and Tsang, Michael and Vaseli, Hooman and Tsang, Teresa and Abolmaesumi, Purang },
                   title = { { Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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. This limitation leads to challenges in ultrasound echocardiography, such as poor visualization of heart valves or foreshortening of ventricles. Clinicians must interpret these images with inherent uncertainty, a nuance absent in machine learning’s one-hot labels. We propose Re-Training for Uncertainty (RT4U), a data-centric method to introduce uncertainty to weakly informative inputs in the training set. This simple approach can be incorporated to existing state-of-the-art aortic stenosis classification methods to further improve their accuracy. When combined with conformal prediction techniques, RT4U can yield adaptively sized prediction sets which are guaranteed to contain the ground truth class to a high accuracy. 
  We validate the effectiveness of RT4U on three diverse datasets: a public (TMED-2) and a private AS dataset, along with a CIFAR-10-derived toy dataset. Results show improvement on all the datasets. Our source code is publicly available at: https://github.com/an-michaelg/RT4U
title: Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography
authors: Gu, Ang Nan Tsang, Michael Vaseli, Hooman Tsang, Teresa Abolmaesumi, Purang