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Parameter Transfer Deep Neural Network for Single-Modal B-Mode Ultrasound-Based Computer-Aided Diagnosis |
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| Authors: | Fei Xiaoyan Shen Lu Ying Shihui Cai Yehua Zhang Qi Kong Wentao Zhou Weijun Shi Jun |
| Institution: | 1.Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information Engineering, Shanghai University, Shanghai, China ;2.Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China ;3.Department of Mathematics, School of Science, Shanghai University, Shanghai, China ;4.Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China ;5.Department of Ultrasound, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China ;6.Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Hefei, China ; |
| Abstract: | Elastography ultrasound (EUS) imaging has shown its effectiveness for diagnosis of tumors by providing additional information about tissue stiffness to the conventional B-mode ultrasound (BUS). However, due to the lack of EUS devices and experienced sonologists, EUS is not widely used, especially in rural areas. It is still a challenging task to improve the performance of the single-modal BUS-based computer-aided diagnosis (CAD) for tumors. In this work, we propose a novel transfer learning (TL)–based deep neural network (DNN) algorithm, named CW-PM-DNN, for the BUS-based CAD by transferring diagnosis knowledge from EUS during model training. CW-PM-DNN integrates both the feature-level and classifier-level knowledge transfer into a unified framework. In the feature-level TL, a bichannel DNN is learned by the cross-weight-based multimodal DL (MDL-CW) algorithm to transfer informative features from EUS to BUS. In the classifier-level TL, a projective model (PM)–based classifier is then embedded to the pretrained bichannel DNN to implement the parameter transfer in the classifier model at the second stage. The back-propagation procedure is then applied to optimize the whole CW-PM-DNN to further improve its performance. Experimental results on two bimodal ultrasound tumor datasets demonstrate that the proposed CW-PM-DNN achieves the best classification accuracy, sensitivity, and specificity of 89.02 ± 1.54%, 88.37 ± 4.72%, and 89.63 ± 4.06%, respectively, for the breast ultrasound dataset, and the corresponding values of 80.57 ± 3.41%, 76.67 ± 3.85%, and 83.94 ± 3.95%, respectively, for the prostate ultrasound dataset. The proposed two-stage TL-based CW-PM-DNN algorithm outperforms all the compared algorithms. It is also proved that the performance of the BUS-based CAD can be significantly improved by transferring the knowledge of EUS. It suggests that CW-PM-DNN has the potential for more applications in the field of medical image–based CAD. |
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