Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer
This addresses the need for non-invasive and efficient prediction in ovarian cancer treatment, though it is incremental as it builds on existing deep learning approaches with multi-view and attention mechanisms.
The paper tackles the problem of predicting residual disease in ovarian cancer from 3D CT images by proposing a Multi-View Attention Learning method, which outperforms existing deep-learning methods on a dataset of 111 patients.
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.