Fast and Fair Medical AI on the Edge through Neural Architecture Search for Hybrid Vision Models


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As edge devices become readily available and indispensable, there is an urgent need for effective and efficient intelligent applications to be deployed widespread. However, fairness has always been an issue, especially in edge medical applications. Although many approaches have been proposed to mitigate the unfairness problem, their edge performance is not desirable. By examining the fairness performance of different network architectures, we observed that compared to pure convolutional neuron network (CNN) architecture, hybrid models with CNN and Vision Transformer (ViT) have exhibited better performance in terms of fairness and accuracy. After further analyzing the feature maps of intermediate layers of CNNs, ViTs, and hybrid models, we found that ViT has a strong ability to extract global information, which contributes to alleviating the unfairness problem. However, ViTs consume large amounts of computational and memory resources, which hinders their application on edge devices. To address the challenges abovementioned, we propose the first hardware-oriented co-design NAS framework to explore hybrid ViT-CNN architecture for the fair dermatology classification, namely HeViFa, which can produce light-weight models for edge devices with low unfairness scores and high classification accuracy. Experimental results show that compared with FaHaNa-Small, HeViFa-Small could search for a hybrid ViT model that reaches 10.57% and 4.03% higher accuracy as well as 0.179 and 0.0403 higher PQD score on Mix and Fitzpatrick17k dataset, repectively, and speed up by 1.21x on Samsung S21 mobile phone, 1.18x on iPhone 13 Pro and 1.37x on Raspberry Pi.
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