# Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images
## 1 Introduction
[14] used a pre-trained 3D-CNN on the LIDC-IDRI dataset to identify nodules in CT scans. Three new untrained layers were added to the existing pre-trained network, a private dataset of 796 patients who underwent CT-guided lung biopsy were used to retrain and test the approach. The biopsy results were used as ground truth labels removing the subjectivity of the annotations by the radiologists that are present in the LIDC-IDRI dataset. The TL of the initial layers of the networks with the retrain on the new dataset achieved an AUC of 0.70. [PERSON] et al. [15; 16] proposed an investigation with multiple ImageNet [17; 18] pre-trained feature extractors and different classifiers tested on the LIDC-IDRI dataset. The highestAUC of 0.931 was achieved with the ResNet-50 [19] architecture and a SVM with a radial basis kernel as a classifier. [PERSON] et al. [20] explored a pulmonary nodule classification method based on the pre-trained ResNet for feature extraction and classification of the pulmonary nodules in an end-to-end manner that was evaluated on LIDC-IDRI and achieved an AUC of 0.912. [PERSON] et al. [21] developed an approach based on a pre-trained VGG-16 model in ImageNet combined with an SVM classifier for a false-positive reduction in pulmonary nodule detection on CT slices from the LIDC-IDRI dataset and obtained an overall accuracy of 0.915. The autoencoder is a dimensionality reduction method that adds the opportunity to extract features using unlabelled data, which allows overcoming one of the biggest limitations in the medical data. Autoencoders extract representative patterns from the images using the image reconstruction mechanism [22]. [PERSON] et al. [23] proposed to use an unsupervised denoising autoencoder to extract deep features from 2D lung nodule slices from the LIDC-IDRI dataset, achieving a classification mean accuracy value of 0.750, using a decision tree as a classifier. [PERSON] et al. [24] used a stacked denoising auto-encoder (SDAE) and constructed a pre-training architecture to use as network initialization for the latter supervised training, which achieved an AUC of 0.984 on the LIDC-IDRI dataset. The work presented in this paper addressed a binary lung nodule malignancy classification by a TL approach based on a trained Convolutional Autoencoder (CAE), using the LIDC-IDRI dataset. Taking advantage of the unsupervised feature learning ability of the CAE while reconstructing nodule 2D images, the relevance of the learned patterns was explored to prevent the overfitting occurrence. Thus, a first experiment was conducted to train the CAE, and then a classification model was developed to distinguish between benign and malignant lung nodules in a 2D perspective. The major contribution of this work relies on three main points: simple architecture, by taking advantage of the computational resources lower consumption; no annotations required on pre-training, allowing to use large available datasets without the huge investment on labeling all data; pre-trained architecture, without the need of explicit design and selection of problem-oriented features that can be used for other tasks.