Fig. 1: Illustration of memristive artificial neural networks (ANNs) and medical imaging system.
From: Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis
a Memristive ANNs. The matrix entries of ANNs are obtained by training and they are usually quantized before being mapped to memristor arrays, resulting in quantization errors. Besides, most ANNs are computed in real number fashion. b–d Memristive medical imaging system. b Medical signal acquisition. Explosive amount of raw data is acquired from medical scanners such as magnetic resonance imaging (MRI) and computed tomography (CT). c Memristive image reconstruction. The matrix entries used in signal processing algorithms here are pre-calculated without training, making them more susceptible to the non-ideal device characteristics of memristors. In addition, their entries are usually expressed in analogue manner with both real and imaginary parts, requiring a completing different mapping strategy onto memristor arrays. d Results for medical diagnosis. Medical images of human body are reconstructed from raw data and then further segmentation and diagnosis can be performed. The cartoon pictures of human organs and medical equipment used in b and d was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.
