False Positive Reduction of Pulmonary Nodule on CT image using Attention-based Multiple Instance Learning

Diagnosis and treatment of multiple pulmonary nodules are clinically essential but challenging. Pulmonary nodule detection is important in early lung cancer detection and diagnosis. False positive reduction (FPR) is a significant stage of pulmonary nodule detection systems. Prior investigations on nodule candidate classification use solitary-nodule approaches, which ignore the relations between nodules. In this study, we propose to use Attention-based Deep Multiple Instance Learning, a variation of supervised learning to recognize true pulmonary nodules among a large group of candidates proposed from the detection stage. By treating the multiple nodules from a different patient, critical relational information between solitary-nodule is extracted and empirically proves the benefit of learning the relations between multiple nodules. An attention layer trained with CNN to replace typical pooling-based aggregation in multiple instance learning (MIL). Experiments of lung nodule FPR on the public LUNA16 dataset validate the effectiveness of the proposed method. The proposed method achieved an accuracy of 99.6%, specificity of 100%, recall of 99.92%, and F1 score of 99.6%. The experimental results reveal that our method can achieve satisfactory performance in FPR.