Liao et al., 2017, Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

# 세줄요약 #

1. Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy.
2. The model consists of two modules, the first one is a 3D region proposal network for nodule detection, which outputs all suspicious nodules for a subject, then the second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities and combines them with a leaky noisy-or gate to obtain the probability of lung cancer for the subject(The two modules share the same backbone network, a modified U-net).
3. The proposed model won the first place in the Data Science Bowl 2017 competition.

 

* Reference: Liao, Fangzhou & Liang, Ming & Li, Zhe & Hu, Xiaolin & Song, Sen. (2017). Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network. IEEE Transactions on Neural Networks and Learning Systems. PP. 10.1109/TNNLS.2019.2892409.