# Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images
## 4 Discussion  
In the study, we proposed a deep structured algorithm to automatically extract features based on a convolutional autoencoder and an end-to-end learning classification network to predict the malignancy risk of nodules in CT images using TL techniques. A baseline experiment was also implemented, where the proposed architecture was trained from the scratch, in order to assess the capability to use the proposed TL strategy for this classification task. The results achieved suggest that the transfer learning approach was able to perform as well as the network trained from scratch, which means that the unsupervised learning ability of the proposed CAE represented a useful knowledge for the classification task, helping the network to avoid overfitting. Considering the previous works, it is not possible to make a fair and direct comparison of the performance results, since the selection of the data and the criteria for final labelling of the nodules were different, which impacts the final performance. The results obtained in the current work did not overcome the performance obtained in some studies but achieve the same high level (above 0.9 of accuracy) using an approach that is not dependent on the massive label data and without needing feature engineering. In fact, the presented approach has several advantages that makes this contribution relevant in the medical image classification: (1) end-to-end approach (automatic feature extraction avoiding the _ad hoc_ feature engineering); (2) unsupervised learning (allowing to use massive datasets without annotations to train the feature extractor) and (3) transfer learning (use the knowledge acquired with an unlabelled dataset to use in a different but related problem). Additionally, studies using TL were usually based on pre-trained ImageNet models, since this is a massive annotated dataset. In a comparison with this more conventional transfer learning strategy, an important advantage provided by the proposed approach relies on the similarities between source and target domains, which is often a problem when applying a model trained on natural images in a medical imaging task like CT scans. This domain gap might compromise the feature extraction since the pre-trained model might not be able to detect the relevant features for the classification task, which might cause an impact on the required fine-tuning depth, alongside with the dataset size required for this deeper fine-tuning. The approach presented in this study allows the use of public CT datasets to train the feature extractor, ensuring that the model can capture the relevant features for the final problem. One of the limitations of this work is the binary output characteristic. The classification performed by the clinicians considers multiple classes since the malignancy evaluation in a clinical environment is not limited by a binary benign/malignant result. Different classes might give additional and useful information to help with diagnosis. As future work, a multi-class solution will be developed. However,  
Figure 5: Testing examples, representing benign and malignant nodules, well and wrongly classified. The MSE value reported for each example represents the mean squared pixel error between the original image and its reconstruction. a very clear annotated dataset must be labeled in order to decrease the label noise introduced by the inter-observer variability of the annotations. With a fine-grained classification, it is expected a higher disagreement between annotators, due to the higher difficult to distinguish between multiple classes. Several scientific societies recommended guidelines for nodule management [33]. The first assessment of the nodule is based on patient history and imaging studies. Further invasive investigation with biopsies is performed for the ones evaluated with a higher risk of malignancy.