  Lung nodule malignancy prediction is an essential step in the early diagnosis
of lung cancer. Besides the difficulties commonly discussed, the challenges of
this task also come from the ambiguous labels provided by annotators, since
deep learning models may learn, even amplify, the bias embedded in them. In
this paper, we propose a multi-view "divide-and-rule" (MV-DAR) model to learn
from both reliable and ambiguous annotations for lung nodule malignancy
prediction. According to the consistency and reliability of their annotations,
we divide nodules into three sets: a consistent and reliable set (CR-Set), an
inconsistent set (IC-Set), and a low reliable set (LR-Set). The nodule in
IC-Set is annotated by multiple radiologists inconsistently, and the nodule in
LR-Set is annotated by only one radiologist. The proposed MV-DAR contains three
DAR submodels to characterize a lung nodule from three orthographic views. Each
DAR consists of a prediction network (Prd-Net), a counterfactual network
(CF-Net), and a low reliable network (LR-Net), learning on CR-Set, IC-Set, and
LR-Set, respectively. The image representation ability learned by CF-Net and
LR-Net is then transferred to Prd-Net by negative-attention module (NA-Module)
and consistent-attention module (CA-Module), aiming to boost the prediction
ability of Prd-Net. The MV-DAR model has been evaluated on the LIDC-IDRI
dataset and LUNGx dataset. Our results indicate not only the effectiveness of
the proposed MV-DAR model in learning from ambiguous labels but also its
superiority over present noisy label-learning models in lung nodule malignancy
prediction.
