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Automatic diagnosing lung cancer from Computed Tomography (CT) scans involvestwo steps: detect all suspicious lesions (pulmonary nodules) and evaluate thewhole-lung/pulmonary malignancy. Currently, there are many studies about thefirst step, but few about the second step. Since the existence of nodule doesnot definitely indicate cancer, and the morphology of nodule has a complicatedrelationship with cancer, the diagnosis of lung cancer demands carefulinvestigations on every suspicious nodule and integration of information of allnodules. We propose a 3D deep neural network to solve this problem. The modelconsists of two modules. The first one is a 3D region proposal network fornodule detection, which outputs all suspicious nodules for a subject. Thesecond one selects the top five nodules based on the detection confidence,evaluates their cancer probabilities and combines them with a leaky noisy-orgate to obtain the probability of lung cancer for the subject. The two modulesshare the same backbone network, a modified U-net. The over-fitting caused bythe shortage of training data is alleviated by training the two modulesalternately. The proposed model won the first place in the Data Science Bowl2017 competition. The code has been made publicly available.

Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network