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AUTOMATED LUNG CANCER DIAGNOSIS USING THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORKS

Abstract

Method

Results

METHODAP (%)
No FP reduction method
HOG + SVM
CNN 2D
CNN 2.5D
CNN 3D		0.0062
0.021
8.9
36.7
44.8
Table 1. Average precision obtained with different false positive reduction methods of the nodule detector.
METHODAUC ROC (%)
Validation set
5-way Siamese cancer predictor
+ 3D mask subtraction (X = 0.82)
Test set
5-way Siamese cancer predictor
+ 3D mask subtraction (X = 0.82)		91.5


91.3*
Table 2. AUC of the ROC curve obtained on the validation and test set with and without post-processing.
* ISBI 2018 lung cancer challenge results can be found here.

Figure 2. Comparison between false positive reduction methods. HOG+SVM and best networks using convolutions in 2D, 2.5D (2D convolutions over 3D candidates) and 3D.