# A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

arXiv: https://arxiv.org/abs/2204.02779 · HTML: https://mlsanity.com/paper/2204.02779/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 69 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 24 of 24)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging](https://mlsanity.com/paper/1909.12475/) (2019)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Unsupervised domain adaptation for medical imaging segmentation with self-ensembling](https://mlsanity.com/paper/1811.06042/) (2018)
- [An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset](https://mlsanity.com/paper/2010.15526/) (2020)
- [Multi-Atlas Segmentation of Biomedical Images: A Survey](https://mlsanity.com/paper/1412.3421/) (2014)
- [The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study](https://mlsanity.com/paper/1911.00515/) (2019)
- [Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient](https://mlsanity.com/paper/2103.06205/) (2021)
- [Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation](https://mlsanity.com/paper/2107.05975/) (2021)
- [DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation](https://mlsanity.com/paper/2012.07950/) (2020)
- [Automatic brain tissue segmentation in fetal MRI using convolutional neural networks](https://mlsanity.com/paper/1906.04713/) (2019)
- [MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation](https://mlsanity.com/paper/2103.13314/) (2021)
- [Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation](https://mlsanity.com/paper/2107.03846/) (2021)
- [Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features](https://mlsanity.com/paper/1709.09075/) (2017)
- [Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI](https://mlsanity.com/paper/2108.04175/) (2021)
- [Distributionally Robust Deep Learning using Hardness Weighted Sampling](https://mlsanity.com/paper/2001.02658/) (2020)
- [Partial supervision for the FeTA challenge 2021](https://mlsanity.com/paper/2111.02408/) (2021)
- [Incompressible image registration using divergence-conforming B-splines](https://mlsanity.com/paper/1907.01593/) (2019)

## Built on by (most cited first, 11 of 11)

- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge](https://mlsanity.com/paper/2505.02784/) (2025)
- [Primitive Simultaneous Optimization of Similarity Metrics for Image Registration](https://mlsanity.com/paper/2304.01601/) (2023)
- [How Do Companies Manage the Environmental Sustainability of AI? An Interview Study About Green AI Efforts and Regulations](https://mlsanity.com/paper/2505.07317/) (2025)
- [Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)](https://mlsanity.com/paper/2308.01318/) (2023)
- [Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures](https://mlsanity.com/paper/2405.20230/) (2024)
- [AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI](https://mlsanity.com/paper/2411.02867/) (2024)
- [Governable AI: Provable Safety Under Extreme Threat Models](https://mlsanity.com/paper/2508.20411/) (2025)
- [Disentangling Hardness from Noise: An Uncertainty-Driven Model-Agnostic Framework for Long-Tailed Remote Sensing Classification](https://mlsanity.com/paper/2601.00278/) (2026)
- [INFANiTE: Implicit Neural representation for high-resolution Fetal brain spatio-temporal Atlas learNing from clinical Thick-slicE MRI](https://mlsanity.com/paper/2605.09977/) (2026)
- [Characterizing Text Branch Sensitivity in Medical Vision-Language Segmentation via Evidence Decoupling](https://mlsanity.com/paper/2609.02663/) (2026)

## Abstract (arXiv)

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of a state-of-the-art backbone AI for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.