Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

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 results. In this review, we offer a comprehensive overview of the prevailing methods proposed to quantify the 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. 

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

Evidential Uncertainty Quantification: A Variance-Based Perspective

As we navigate towards integrating deep learning methods in the real clinic, a safety concern lies in whether and how the model can express its own uncertainty when making predictions. In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. 

Deep evidential learning for radiotherapy dose prediction

Purpose. The purpose of this work is to produce uncertainty estimation for the registration task between MR and CBCT based on the joint synthesis and registration network. This novel framework aims to equip surgeons with robust tools for assessing the fidelity of registration, enabling informed decisions regarding the volume alignment, particularly in regions where uncertainty is prevalent. Methods. The prediction of deformation fields is framed as a regression problem, and therefore allows Evidential Deep Learning (EDL) for uncertainty estimation. By utilizing a loss function grounded in likelihood, the network is compelled to simultaneously learn the parameters of EDL and generate precise predictions. This adaptive approach is tailored specifically for the registration task, rendering the estimation method notably flexible for unsupervised registration. Results. The visualization of the uncertainty map demonstrates areas where the registered ventricle deviates from the ground truth alignment. Additionally, the incorporation of uncertainty assessment enhances registration performance, as evidenced by quantitative evaluations across seven distinct brain regions. Comparative analysis between networks trained with and without uncertainty underscores the performance of the uncertainty-aware model in achieving more accurate registrations. Conclusions. The preliminary results show that incorporating uncertainty estimation into registration model leads to improved registration performance. Furthermore, the estimated uncertainty map is also useful for surgeons to evaluate the reliability of registered images.

Deep evidential uncertainty estimation in joint synthesis and registration of MRI and cone-beam CT images

Purpose: To derive a multinomial probability function and quantitative measures of the data and epistemic uncertainty as direct output of a 3D U-Net segmentation network. Approach: A set of T1 brain MRI images were downloaded from the Connectome Project and segmented using FMRIB’s FAST algorithm to be used as ground truth. A 3D U-Net neural network was trained with sample sizes of 200, 500, and 898 T1 brain images using a loss function defined as the negative logarithm of the likelihood based on a derivation of the definition of the multinomial probability function. From this definition, the epistemic and aleatoric uncertainty equations were derived and used to quantify maps of the uncertainty along with tissue segmentations. Results: Maps of the tissue segmentation along with the epistemic and aleatoric uncertainty, per voxel, are presented. The uncertainty decreased based on the increasing number of training data used to train the neural network. The neural network trained with 898 volumes resulted in uncertainty maps that were high primarily in the tissue boundary regions. The epistemic and aleatoric uncertainty were averaged over all test data (connectome and tumor separately), and the epistemic uncertainty showed a decreasing trend, as expected, with increasing numbers of data used to train the model. The aleatoric uncertainty showed a similar trend which was also expected as the aleatoric uncertainty is not expected to be as dependent on the number of training data. Conclusion: The derived uncertainty equations from a multinomial probability distribution were able to quantify the aleatoric and epistemic uncertainty per voxel and are applicable for all two-dimensional and three-dimensional neural networks. 

Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation

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Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation