  Early diagnosis of lung cancer is a key intervention for the treatment of
lung cancer computer aided diagnosis (CAD) can play a crucial role. However,
most published CAD methods treat lung cancer diagnosis as a lung nodule
classification problem, which does not reflect clinical practice, where
clinicians diagnose a patient based on a set of images of nodules, instead of
one specific nodule. Besides, the low interpretability of the output provided
by these methods presents an important barrier for their adoption. In this
article, we treat lung cancer diagnosis as a multiple instance learning (MIL)
problem in order to better reflect the diagnosis process in the clinical
setting and for the higher interpretability of the output. We chose radiomics
as the source of input features and deep attention-based MIL as the
classification algorithm.The attention mechanism provides higher
interpretability by estimating the importance of each instance in the set for
the final diagnosis.In order to improve the model's performance in a small
imbalanced dataset, we introduce a new bag simulation method for MIL.The
results show that our method can achieve a mean accuracy of 0.807 with a
standard error of the mean (SEM) of 0.069, a recall of 0.870 (SEM 0.061), a
positive predictive value of 0.928 (SEM 0.078), a negative predictive value of
0.591 (SEM 0.155) and an area under the curve (AUC) of 0.842 (SEM 0.074),
outperforming other MIL methods.Additional experiments show that the proposed
oversampling strategy significantly improves the model's performance. In
addition, our experiments show that our method provides an indication of the
importance of each nodule in determining the diagnosis, which combined with the
well-defined radiomic features, make the results more interpretable and
acceptable for doctors and patients.
