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DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.491

Attention Guided Multi-task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction

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  • 1. School of Medical Imaging, Xuzhou Medical University, Jiangsu, 221000, China；2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Jiangsu, 215163, China；3. The Second Affiliated Hospital of Suzhou University, Jiangsu, 215000, China

Online published: 2023-11-30

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Abstract

Prediction of pelvic lymph node metastasis (PLNM) based on quantitative preoperative magnetic resonance imaging features of prostate cancer primary tumor is an important reference for treatment planning. However, current prediction methods inadequately capture the heterogeneity information within the primary tumor, resulting in insufficient correlation between extracted quantitative image features and PLNM prediction. In response to the aforementioned issues, an attention guided multi-task learning network with tumor segmentation as an auxiliary task is proposed for PLNM prediction. Firstly, within the tumor segmentation network, a multi-branch anisotropic large kernel attention module is introduced, where a larger receptive field is obtained through different branches and anisotropic large convolutional kernels, effectively capturing both local and global tumor information. Subsequently, within the PLNM prediction network, a multi-scale feature interaction fusion attention module is introduced to hierarchically fuse and select features from multiple scales. Experimental results on a dataset of 320 cases demonstrate that the proposed method achieves superior AUPRC (85.44%±2.04%) and AUROC (91.86%±2.18%) values compared to state-of-the-art methods and multi-task methods.

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Cite this article

ZHANG Zhiyuan, HU Jisu, ZHANG Yueyue, QIAN Xusheng, ZHOU Zhiyong, DAI Yakang . Attention Guided Multi-task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction[J]. Journal of Shanghai Jiaotong University, 0 : 0 . DOI: 10.16183/j.cnki.jsjtu.2023.491

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