Unsupervised Feature Mapping via Stacked Sparse Autoencoder for Automated Detection of Large Pulmonary nodules in CT Images
We present a novel and efficient false positive reduction stage, using stacked sparse autoencoder, for the automatic detection of large nodules in computed tomography (CT) images. The discriminative features are automatically learnt in unsupervised manner. The initial candidates are segmented using candidate detection method specifically designed for the large nodules. For each candidate, 3D grayscale clusters are computed and, are later resized into a uniform size of 10 × 10 × 5 for feature mapping. Finally, a softmax layer is used for the binary classification. Data augmentation, sparsity regularization, and L2 weight regularization are applied to overcome the generalization issue. On 899 CT scans taken from LIDC-IDRI, our method yields a high detection sensitivity of 90 % with only 4 false positives per scan and an area under receiver operating curve of 0.983. An external validation on a completely independent dataset from PCF is also performed to evaluate the potency of the proposed method. We showed that the proposed stacked sparse autoencoder is efficient enough to be accommodated as a false positive reduction phase in a computer-aided-detection system.
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