# Prototype Learning for Explainable Brain Age Prediction

arXiv: https://arxiv.org/abs/2306.09858 · HTML: https://mlsanity.com/paper/2306.09858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 25 of 25)

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- [Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions](https://mlsanity.com/paper/1710.04806/) (2017)
- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
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- [XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations](https://mlsanity.com/paper/2103.10663/) (2021)
- [Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker](https://mlsanity.com/paper/1612.02572/) (2016)
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- [Towards Interpretable Deep Metric Learning with Structural Matching](https://mlsanity.com/paper/2108.05889/) (2021)
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- [ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans](https://mlsanity.com/paper/2103.02561/) (2021)
- [Challenges for machine learning in clinical translation of big data imaging studies](https://mlsanity.com/paper/2107.05630/) (2021)
- [INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples](https://mlsanity.com/paper/2208.00457/) (2022)
- [Patch-based Brain Age Estimation from MR Images](https://mlsanity.com/paper/2008.12965/) (2020)
- [Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images](https://mlsanity.com/paper/2103.13482/) (2021)
- [Regression Metric Loss: Learning a Semantic Representation Space for Medical Images](https://mlsanity.com/paper/2207.05231/) (2022)

## Abstract (arXiv)

The lack of explainability of deep learning models limits the adoption of such models in clinical practice. Prototype-based models can provide inherent explainable predictions, but these have predominantly been designed for classification tasks, despite many important tasks in medical imaging being continuous regression problems. Therefore, in this work, we present ExPeRT: an explainable prototype-based model specifically designed for regression tasks. Our proposed model makes a sample prediction from the distances to a set of learned prototypes in latent space, using a weighted mean of prototype labels. The distances in latent space are regularized to be relative to label differences, and each of the prototypes can be visualized as a sample from the training set. The image-level distances are further constructed from patch-level distances, in which the patches of both images are structurally matched using optimal transport. This thus provides an example-based explanation with patch-level detail at inference time. We demonstrate our proposed model for brain age prediction on two imaging datasets: adult MR and fetal ultrasound. Our approach achieved state-of-the-art prediction performance while providing insight into the model's reasoning process.