Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification

2022/07/05 by Jeon, Jaeik, Jang, Yeonggul, Hong, Youngtaek +2
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2207.01868

Abstract

Medical imaging, including MRI, CT, and Ultrasound, plays a vital role in clinical decisions. Accurate segmentation is essential to measure the structure of interest from the image. However, manual segmentation is highly operator-dependent, which leads to high inter and intra-variability of quantitative measurements. In this paper, we explore the feasibility that Bayesian predictive distribution parameterized by deep neural networks can capture the clinicians' inter-intra variability. By exploring and analyzing recently emerged approximate inference schemes, we evaluate whether approximate Bayesian deep learning with the posterior over segmentations can learn inter-intra rater variability both in segmentation and clinical measurements. The experiments are performed with two different imaging modalities: MRI and ultrasound. We empirically demonstrated that Bayesian predictive distribution parameterized by deep neural networks could approximate the clinicians' inter-intra variability. We show a new perspective in analyzing medical images quantitatively by providing clinical measurement uncertainty.

Related

• Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
• Finding novelty with uncertainty
• On the Effect of Inter-observer Variability for a Reliable Estimation of\n Uncertainty of Medical Image Segmentation
• BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability
• Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks