Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing

• URL: http://arxiv.org/abs/2005.05431v1
• Date: Mon, 11 May 2020 20:56:37 GMT
• Title: Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing
• Authors: Neil Getty, Thomas Brettin, Dong Jin, Rick Stevens, Fangfang Xia
• Abstract summary: We present a capsule network that explicitly learns a representation robust to rotation and affine transformation. Second, we leverage the latest domain adaptation techniques to achieve a new state-of-the-art accuracy. Third, we design a spiking neural network trained on the Intel Loihi neuromorphic chip.
• Score: 1.43494686131174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We explore three representative lines of research and demonstrate the utility of our methods on a classification benchmark of brain cancer MRI data. First, we present a capsule network that explicitly learns a representation robust to rotation and affine transformation. This model requires less training data and outperforms both the original convolutional baseline and a previous capsule network implementation. Second, we leverage the latest domain adaptation techniques to achieve a new state-of-the-art accuracy. Our experiments show that non-medical images can be used to improve model performance. Finally, we design a spiking neural network trained on the Intel Loihi neuromorphic chip (Fig. 1 shows an inference snapshot). This model consumes much lower power while achieving reasonable accuracy given model reduction. We posit that more research in this direction combining hardware and learning advancements will power future medical imaging (on-device AI, few-shot prediction, adaptive scanning).

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