论文标题
通过深度设定的注意变压器之间多个肺结核之间的关系学习
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers
论文作者
论文摘要
多种肺结核的诊断和治疗在临床上很重要，但具有挑战性。对结节表征的先前研究使用了多个结节性患者的孤立结节方法，这些方法忽略了结节之间的关系。在这项研究中，我们提出了一种多个实例学习（MIL）方法，并从经验上证明了学习多个结节之间的关系的好处。通过处理与整个患者的多个结节，提取了孤立结节体素之间的关键关系信息。据我们所知，这是学习多个肺结核之间关系的第一项研究。受到自然语言处理（NLP）域的最新进展的启发，我们引入了一个配备3D CNN的自我发明变压器，名为{nodulesat}，以替换多个实例学习中的典型基于池的聚合。关于LUNA16数据库的肺结节假阳性的广泛实验，以及对LIDC-IDRI数据库的恶性分类，验证了该方法的有效性。
Diagnosis and treatment of multiple pulmonary nodules are clinically important but challenging. Prior studies on nodule characterization use solitary-nodule approaches on multiple nodular patients, which ignores the relations between nodules. In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules. By treating the multiple nodules from a same patient as a whole, critical relational information between solitary-nodule voxels is extracted. To our knowledge, it is the first study to learn the relations between multiple pulmonary nodules. Inspired by recent advances in natural language processing (NLP) domain, we introduce a self-attention transformer equipped with 3D CNN, named {NoduleSAT}, to replace typical pooling-based aggregation in multiple instance learning. Extensive experiments on lung nodule false positive reduction on LUNA16 database, and malignancy classification on LIDC-IDRI database, validate the effectiveness of the proposed method.
