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Saliency Transfer Learning and Central-Cropping Network for Prostate Cancer Classification

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Abstract

Classifying the malignancy of prostate lesions from MRI images is crucial in diagnosing prostate cancer at the early stage. In clinical examination, radiologists usually focus on the most salient and distinctive regions to diagnose. However, in many state-of-the-art CNN based methods, the conventional convolution operation extracts the features equally importantly, which leads to an excessive feature learning process on the uninterested regions. To address this challenge, we propose a saliency transfer learning network that allows the model to focus on the salient and influential regions automatically. Moreover, a pyramid central-crop pooling scheme is employed to extract the multi-scale, centric-visual, and salient features from different layers. To validate the effectiveness of the proposed model, extensive experiments are conducted on prostate cancer and non-cancer MRI dataset, the experimental results demonstrate that our proposed model could gain competitive performance (Accuracy 94.9%, Sensitivity 96.7%, Specificity 93.5%, AUC 0.989) on this classification task.

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Acknowledgements

This work was supported by the Shanghai Sailing Program (21YF1431600), and the General Program of the National Natural Science Foundation of China (NSFC) under Grant 62102259. The authors declared that they have no conflicts of interest in this work.

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Authors and Affiliations

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China

    Guokai Zhang & Aijun Zhang

  2. Industry Development and Promotion Center Ministry of Industry and Information Technology of the People’s Republic of China, Beijing, China

    Mengpei Jia

  3. Department of Molecular Imaging, Qingdao Central Hospital, Qingdao University, Qingdao, China

    Lin Gao

  4. School of Computing, National University of Singapore, Singapore, Singapore

    Jihao Luo

  5. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

    Yongyong Chen

  6. Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China

    Peipei Shan

  7. Department of Radiology, Shanghai Tenth People’s Hospital, Shanghai, China

    Binghui Zhao

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  1. Guokai Zhang
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  2. Mengpei Jia
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  3. Lin Gao
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  4. Jihao Luo
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  5. Aijun Zhang
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  6. Yongyong Chen
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  8. Binghui Zhao
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Correspondence to Yongyong Chen, Peipei Shan or Binghui Zhao.

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Zhang, G., Jia, M., Gao, L. et al. Saliency Transfer Learning and Central-Cropping Network for Prostate Cancer Classification. Neural Process Lett 55, 2391–2403 (2023). https://doi.org/10.1007/s11063-022-10999-z

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