Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer

Xiangneng Gao1; Shulan Ruan2; Jun Shi2; Guoqing Hu3; Wei Wei3

10.1109/SMC53992.2023.10394014

2023

1062-922X

979-8-3503-3703-7

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

653-658

1-4 Oct. 2023

Honolulu, Oahu, HI, USA

In the treatment of ovarian cancer, precise residual disease prediction is significant for clinical and surgical decision-making. However, traditional methods are either invasive (e.g., laparoscopy) or time-consuming (e.g., manual analysis). Recently, deep learning methods make many efforts in automatic analysis of medical images. Despite the remarkable progress, most of them underestimated the importance of 3D image information of disease, which might brings a limited performance for residual disease prediction, especially in small-scale datasets. To this end, in this paper, we propose a novel Multi-View Attention Learning (MuVAL) method for residual disease prediction, which focuses on the comprehensive learning of 3D Computed Tomography (CT) images in a multi-view manner. Specifically, we first obtain multi-view of 3D CT images from transverse, coronal and sagittal views. To better represent the image features in a multi-view manner, we further leverage attention mechanism to help find the more relevant slices in each view. Extensive experiments on a dataset of 111 patients show that our method outperforms existing deep-learning methods.

Learning systems Laparoscopes Deep learning Three-dimensional displays Computed tomography Decision making Surgery

第一

IEEE

1.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
3.Department of Radiology, First Affiliated Hospital of USTC, Hefei, China

Xiangneng Gao,Shulan Ruan,Jun Shi,et al. Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer[C],2023:653-658.