A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods

The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the result. In this review, we offer a comprehensive overview of prevailing methods proposed to quantify uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.

Learning Continuous-Time Dynamics by Stochastic Differential Networks

Learning continuous-time stochastic dynamics from sparse or irregular observations is a fundamental and essential problem for many real-world applications. However, for a given system whose latent states and observed data are high-dimensional, it is generally impossible to derive a precise continuous-time stochastic process to describe the system behaviors. To solve the above problem, we apply Variational Bayesian method and propose a flexible continuous-time framework named Variational Stochastic Differential Networks (VSDN), which can model high-dimensional nonlinear stochastic dynamics by deep neural networks. VSDN introduces latent states to modulate the estimated distribution and defines two practical methods to model the stochastic dependency between observations and the states. The first variant, which is called VSDN-VAE, incorporates sequential Variational Auto-Encoder (VAE) to efficiently model the distribution of the latent states. The second variant, called VSDN-SDE, further extends the model capacity of VSDN-VAE by learning a set of Stochastic Differential Equations (SDEs) to fully describe the state transitions. Through comprehensive experiments on symbolic MIDI and speech datasets, we show that VSDNs can accurately model the continuous-time dynamics and achieve remarkable performance on challenging tasks, including online prediction and sequence interpolation.