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.

翻译：在卵巢癌治疗中，精确的残留病灶预测对临床和手术决策具有重要意义。然而，传统方法要么具有侵入性（如腹腔镜检查），要么耗时较长（如人工分析）。近年来，深度学习方法在医学影像自动分析领域取得诸多进展。尽管已有显著进步，大多数方法低估了疾病三维影像信息的重要性，这可能导致残留病灶预测性能受限，尤其在小规模数据集上。为此，本文提出一种新颖的"多视角注意力学习"（MuVAL）方法用于残留病灶预测，该方法通过多视角方式综合学习三维计算机断层扫描（CT）影像。具体而言，我们首先从横断面、冠状面和矢状面获取三维CT影像的多视角视图。为更好地以多视角方式表征影像特征，我们进一步利用注意力机制帮助寻找每个视角中更具相关性的切片。在包含111名患者的数据集上的大量实验表明，我们的方法优于现有深度学习方法。