# ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data

## TL;DR

This paper introduces ChronoMID, a cross-modal neural network approach for analyzing 3-D temporal medical imaging data, achieving high accuracy in classifying bone disease in mice by leveraging temporal features.

## Contribution

It applies cross-modal CNNs to medical imaging, incorporating temporal information via timestamps and difference images, demonstrating significant accuracy improvements.

## Key findings

- Models with temporal features outperform baseline CNNs.
- Top model achieves 99.54% accuracy on unseen data.
- Training on over 75,000 images validates effectiveness.

## Abstract

ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data. Specifically, this paper presents and compares alternative approaches - timestamps and difference images - to incorporate temporal information for the classification of bone disease in mice, applied to micro-CT scans of mouse tibiae. Whilst much previous work on diseases and disease classification has been based on mathematical models incorporating domain expertise and the explicit encoding of assumptions, the approaches given here utilise the growing availability of computing resources to analyse large datasets and uncover subtle patterns in both space and time. After training on a balanced set of over 75000 images, all models incorporating temporal features outperformed a state-of-the-art CNN baseline on an unseen, balanced validation set comprising over 20000 images. The top-performing model achieved 99.54% accuracy, compared to 73.02% for the CNN baseline.