ML-DSVM plus : A meta-learning based deep SVM plus for computer-aided diagnosis

Transfer learning (TL) can improve the performance of a single-modal medical imaging-based computer -aided diagnosis (CAD) by transferring knowledge from related imaging modalities. Support vector ma-chine plus (SVM + ) is a supervised TL classifier specially designed for TL between the paired data in the source and target domains with shared labels. In this work, a novel deep neural network (DNN) based SVM + (DSVM + ) algorithm is proposed for single-modal imaging-based CAD. DSVM + integrates the bi-channel DNNs and SVM + classifier into a unified framework to improve the performance of both feature representation and classification. In particular, a new coupled hinge loss function is developed to con-duct bidirectional TL between the source and target domains, which further promotes knowledge trans-fer together with the feature representation under the guidance of shared labels. To alleviate the overfit-ting caused by the increased parameters in DNNs for limited training samples, the meta-learning based DSVM + (ML-DSVM + ) is further developed, which designs randomly selecting samples from the training data instead of other CAD tasks for meta-tasks. This sampling strategy also can avoid the issue of class imbalance. ML-DSVM + is evaluated on three medical imaging datasets. It achieves the best results of 88.26 +/- 1.40%, 90.45 +/- 5.00%, and 87.63 +/- 5.56% on accuracy, sensitivity and specificity, respectively, on the Bimodal Breast Ultrasound Image dataset, 90.00 +/- 1.05%, 72.55 +/- 3.87%, and 96.40 +/- 2.26% of the correspond-ing indices on the Alzheimer's Disease Neuroimaging Initiative dataset, and 85.76 +/- 3.12% of classification accuracy, 88.73 +/- 7.22% of sensitivity, and 82.60 +/- 1.56% of specificity for the Autism Brain Imaging Data Exchange dataset.(c) 2022 Elsevier Ltd. All rights reserved.