On-device Training for Breast Ultrasound Image Classification

Abstract

Most on-device AI pre-trained a neural network model in cloud-based server then deployed to edge device for inference. On-device training not only can build personalized model, but also can do distributed training like federated learning to train accurate models from scratch using small updates from many devices. In this work, we implement the semi-supervised convolutional neural network based on successive subspace learning and use a dataset of breast ultrasound (BUS) images to demonstrate a proof of concept of true on-device training. An important advantage of such network is that we can extract the key feature vectors with CNN network architectures without the need of backpropagation computation made it suitable for portable ultrasound. So it can acquire the ultrasound image and train the CNN classifier on the portable device without cloud-based server. We evaluate the model by using a set of BUS images that includes benign and malignant breast tumors. We obtain 94.8% accuracy with this study and demonstrate the applicablility of the proposed on-device training model to improve the diagnosis of BUS images.