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ChronoMID builds on the success of cross-modal convolutional neural networks(X-CNNs), making the novel application of the technique to medical imagingdata. Specifically, this paper presents and compares alternative approaches -timestamps and difference images - to incorporate temporal information for theclassification of bone disease in mice, applied to micro-CT scans of mousetibiae. Whilst much previous work on diseases and disease classification hasbeen based on mathematical models incorporating domain expertise and theexplicit encoding of assumptions, the approaches given here utilise the growingavailability of computing resources to analyse large datasets and uncoversubtle patterns in both space and time. After training on a balanced set ofover 75000 images, all models incorporating temporal features outperformed astate-of-the-art CNN baseline on an unseen, balanced validation set comprisingover 20000 images. The top-performing model achieved 99.54% accuracy, comparedto 73.02% for the CNN baseline.

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