Abstract
This short paper describes our contribution to the LNDb - Grand Challenge on automatic lung cancer patient management [1]. We only participated in Sub-Challenge A: Nodule Detection. The officially stated goal of this challenge is From chest CT scans, participants must detect pulmonary nodules. We developed a computer-aided detection (CAD) system for the identification of small pulmonary nodules in screening CT scans. The two main modules of our system consist of a CNN based nodule candidate detection, and a neural classifier for false positive reduction. The preliminary results obtained on the challenge database is discussed.
In this work, we developed an Ensemble learning pipeline using state of the art convolutional neural networks (CNNs) as base detectors. In particular, we utilize the 3D versions of SE-ResNet18 and DPN68. Much like classical bagging, base learners were trained on 10 stratified data-set folds (the LUNA16 patient-level dataset splits) generated by bootstrapping both our training set (LUNA16) and the challenge provided training set. Furthermore, additional variation was introduced by using different CNN architectures. Particularly, we opted for an exhaustive search of the best detectors, consisting mostly of DPN68 [2] and SE-ResNet18 [3] architectures.
We unfortunately joined the competition late, and we did not train our system on the corpus provided by the organizers and therefore we only run inference using our LIDC-IDRI trained model. We do realize this is not the best approach.
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Katz, O., Presil, D., Cohen, L., Schwartzbard, Y., Hoch, S., Kashani, S. (2020). Pulmonary-Nodule Detection Using an Ensemble of 3D SE-ResNet18 and DPN68 Models. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_33
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