Convolutional neural networks (CNNs) often require very large datasets for robust training and evaluation. As an alternative approach, we introduce deep learning diffusion fingerprinting (DLDF), which treats every voxel as an independent data point, rather than using whole images or patches. We use DLDF to classify diffusion-weighted imaging voxels in a mouse model of glioblastoma, both prior to and in response to Temozolomide chemotherapy. We show that, even with limited training, DLDF can automatically segment brain tumours from normal brain, can distinguish between young and older tumours and that DLDF can detect if a tumour has been treated with chemotherapy.
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