Uncertainty Estimation in Cardio Landmark Detection and Heart Disease Diagnosis on Chest X-Ray Images

Landmark detection using chest X-ray images is often a time-consuming task performed by radiologists, followed by the calculation of cardiometric indices and the diagnosis of heart diseases. The goal of this research is to estimate uncertainty in data and knowledge, which leads to increased accuracy and interpretability in models that predict landmark positions and classify human body states. To achieve this, data from about 800 X-ray images labeled by four practicing radiologists was utilized. An uncertainty-aware negative log-likelihood loss was proposed to address both regression and classification problems. The proposed models were trained to predict the mean and log variance of a normal distribution for each input image, with the variance value estimating the uncertainty of the prediction. Natural data uncertainty was also measured from labels provided by different radiologists and implemented in the form of a label transform for the coordinates of the landmarks. The classification models demonstrated good correspondence with methods such as Monte Carlo dropout and deep ensembling, which account for knowledge uncertainty. The proposed uncertainty-aware models achieved the best results in both landmark detection and heart disease diagnosis, with accuracy improvements of 3% and 4.61%, respectively. The proposed methods are effective and easy to use, making their application to other medical problems a subject of interest for further research.