  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.
