# Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images
## 1 Introduction  
Lung cancer is on the top of cancer-related mortality numbers worldwide [1; 2]. Only 16% of lung cancer cases are diagnosed as local stage tumors. In these cases, patients have a five-year survival rate of more than 50%; however, when diagnosed in an advanced stage, the chances of a five-year survival decrease to 5%. Thus, achieving an earlier diagnosis is critical to increase survival rate, and systems able to provide screening support might play an important role. As a non-invasive method, computed tomography (CT) images have shown the ability to provide valuable information on tumor status, rising opportunities to the development of computer-aided diagnoses (CAD) systems able to provide an automatic assessment of lung nodules malignancy risk to help the clinical decision. Considering the use of qualitative data, factors like the high interobserver variability associated with the visual assessment of relevant characteristics, and the amount of radiological data to be analyzed makes the development of completely automatic systems a more attractive approach. Several methods based on convolutional neural networks have been proposed to investigate the ability to distinguish between malignant and benign pulmonary nodules, taking advantage of the ability to directly detect relevant patterns with abstract and complex imaging manifestations [3]. Several previous works proposed deep learning-based solutions for lung nodule malignancy classification using the Lung Image Database Consortium image collection (LIDC-IDRI) [4; 5], which is a public dataset of thoracic CT scans with expert annotations, and the most commonly used to develop AI-based solutions for lung cancer. [PERSON] et al. [6] proposed a hierarchical learning framework to capture the nodule heterogeneity by utilizing a Convolutional neural network (CNN) to extract features and a random forest classifier for the final classification with the highest accuracy of 0.868. [PERSON] et al. [7] obtained an accuracy of 0.919 using a CNN to extract the features and a support vector machine (SVM) for the final classification. [PERSON] et al. [8] compared the performance obtained with 2D and 3D CNN implementations, achieving a mean area under the curve (AUC) of 0.937 for 2D analysis and an AUC of 0.947 using 3D inputs. [PERSON] et al. [9] proposed a comparison between a CNN, a deep neural network (DNN), and a stacked autoencoder (SAE) for the classification of benign or malignant lung nodules, and the CNN showed the highest performance, with an accuracy of 0.842. [PERSON] et al. [10] developed an algorithm that uses a deep convolutional neural network to automatically learn the feature representation of nodules on a 2D analysis, fuses this information with other more common features (shape, texture), and obtained an AUC of 0.966. A similar approach was developed by [PERSON] et al. [11] that combines the deep learning CNN features with radiomics features as input in a random forest classifier and obtained an accuracy of 0.990. The success of deep learning-based feature extraction is due to the ability of not only taking the information of different conventional semantic features (such as shape or margins) or radiomic features (texture or histogram-based properties) but also taking into account abstract features, where deeper levels provide more complex and abstract knowledge [12; 13]. Despite offering successful approaches, deep learning models require a large number of training data to be able to generalize over unseen images, and the lack of publicly available data is often a problem in the majority of medical applications. One strategy to overcome this issue is by using transfer learning (TL), a learning method that consists of applying a network already trained for a different task. By taking advantage of general patterns learned in the first layers, this technique reduces the number of trainable parameters alongside the necessary dataset size. Only a few and most recent works have explored this approach for this classification task. [PERSON] et al.