  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.
