In the present study, we applied a GAN-based model with double discriminators to generate GGN in low-dose CT scans. We benchmarked the performance of the model using a qualitative (VTT with clinicians) and a quantitative approach (radiomics).
To our knowledge, only one previous study proposed the use of GANs to generate lung lesions and performed a VTT , which showed that 67% and 100% of the fake nodules were marked as real by two radiologists, respectively. Differences exist between this study and our study: in the VVT of the cited study , the radiologists reviewed the generated lesions, but the surrounding tissues or the entire lungs were not included in the field of view. Moreover, the surrounding tissues and the lung background that has relationship with nodules were not considered when training and generating the nodules. Conversely, we generated GGNs from the whole lung to use the anatomical dependence with the background tissue . However, the relatively small size of our study compared to the previous research  probably influenced the results of the visual Turing test.
Based on our VTT evaluation, we have shown that GAN-generated lung lesions have the potential to be very consistent with real lesions. This gives us the opportunity to use GAN-generated data to solve real-world problems, such as using the generated data to train and test junior doctors, especially for hospitals that do not have large cohort datasets, long-time established picture archiving and communication systems, as privacy-preserving synthetic open datasets for research purposes.
More than half of the radiomic features were not statistically different between DL-generated and real nodules, proving that the generated GGNs are acquiring or learning detailed features from the real sample. Furthermore, these consistent radiomic features cover all classes, which could support the conclusion that the proposed approach mimics different aspects of real nodules. Conversely, one third of the features in this study showed significant differences in the distribution between the generated and real GGNs. Based on the radiomics results and the clinicians’ opinion, we think that the low complexity of the generated GGNs is the main reason for the discrepancy between the generated and real GGNs. For example, the p-value of the radiomic features coarseness (which can measure the spatial change rate) and complexity (which can measure the non-uniformity of local grey levels) between real and synthetic GGNs are close to 0, supporting our hypothesis. We hypothesise the following explanations: (i) the data source is derived from public databases that have low resolution and lots of noise, and (ii) we did not optimise the training process by specifically including radiomics features in the loss function.
Based on the radiomics results, we built a “radiomics physician” to discriminate between real and generated GGNs, which interestingly is generally consistent with the discriminatory ability of real physicians. It is worth noting that the “radiomics physician” model was trained based on a sample of 100 cases, and the physicians have more than 5 years of experience. Overall, it is a challenging task to discriminate between real and generated GGNs for “radiomics physicians” and real physicians.
Finally, we wanted to test how data augmentation with GAN will affect the detection accuracy of a CAD system. Figure 6 shows that adding synthetic GGNs to the original dataset improves the performance of our DL CAD system. However, there was no significant contribution when the size of the training dataset is under 10% and over 70% of the original sample size. We hypothesise that when the training data is under 10%, there is an insufficient number of samples to train the GAN. A GAN trained on only a few samples cannot synthesise the rich diversity and complexity of real GGNs. Based on the results (Fig. 6), we conclude that the performance of the DL model increases with the sample size in certain ranges of real data samples. However, as shown in Fig. 6, the performance of the DL model cannot be improved after a threshold value larger than the sample size, which is the plateau of the model. Specifically, for effective dataset size to train a GAN, around 50% of training data which include around 100 samples of GGN has the biggest increase in accuracy of the classification model when synthetic GGN are added. Overall, from our experiment, we found that:
Synthetic data has the ability to increase the performance of a DL model unless only a few training samples can be used;
From the perspective of cost and effectiveness, around 100 samples are sufficient to develop a GAN model that can generate realistic GGNs to significant improve the performance of the detection GGN model.
This study has some limitations. First, we used a public dataset for training the model, but we want to extend the work to other datasets. In future studies, we will add high-resolution data from our centre for model enhancement. Second, we only focused on GGNs, because of their lower incidence compared to other types of nodules. However, the dimension and density variation of the included GGNs is limited, which has the potential risk of obtaining optimistic radiomic assessment results. We will perform transfer learning to generate lung nodules and tumours in the future based on the model in this study. Furthermore, the diagnosis of malignant GGN is a challenging task for clinical practice. However, in this study, we did not generate benign or malignant GGN. To address this issue, we are collecting data from the real world with follow-up endpoints and trying to generate qualitative GGN, especially malignant GGN.
Third, we generated only two-dimensional samples. However, generating three-dimensional (3D) images is costly for model training, first, because 3D GANs have a larger number of parameters which need more training data and also have a significantly higher requirement in hardware when the input data has large scale such as CT images. In the future work, we will consider the model compression to decrease the requirement of hardware and the size of dataset for training the 3D GAN. We tried to perform our visual Turing tests by getting closer as much as possible to a real clinical scenario. Nevertheless, it was out of the scope of this study to integrate our DL models within the clinical workstations available to our radiologists. As proof-of-concept, we proposed to our radiologists the generated and real pulmonary nodules as two-dimensional axial CT images in the standard lung window. Future work will include the production of the generated nodules in standard DICOM formats in all the 3D projections. We are also investigating the possibility to invest in the development of a cloud-based platform to homogenise visual Turing tests for similar experiments. In addition, we did not evaluate the morphological features between the generated and real GGNs.
Fourth, we have not discussed the trend of data requirement for different task, such as what happens when the quality of data is decreased, how many data points need to be added when the target size us increased, and whether different sources such as CT and magnetic resonance imaging influence the dataset requirements. In the future work, we will design experiments to figure out the connection between the data requirement and different tasks.
Fifth, according to the results of the radiomics part, there are still considerable differences between the real and generated GGO, and more than one third of the radiomic feature values were different, which may be a reflection that the GAN method proposed in this study is not optimal. Based on this result, there is still much potential for improvement of our algorithm, with a particular focus on improving the level of complexity of the textures.
Sixth, we did not conduct interobserver and intraobserver testing and the degree of disagreement between different readers was not assessed. On the other hand, in our experience, the differences between the readers (physicians) included in this study were limited to the same broad category, i.e., real or fake. For example, nodules labelled as “confidently real” by one physician have the possibility of being labelled as “leaning real” instead of “confidently/leaning fake” by other physicians.
Finally, despite the GANs are an elegant data generation mechanism gaining more and more popularity in the medical field, most of them still present a high level of complexity compared for example to traditional DL algorithms such as convolutional neural networks. For example, there is no consensus on the most appropriate metric to be used to stop the training at the best point (global minimum of the loss function). This will sometimes lead to a not satisfactory quality of the generated data. Especially when dealing with medical images, the risk of introducing novel, undesired artefacts, and blurry images is not negligible.
In conclusion, in this study, we used GANs to generate GGN and validated these by four physicians and radiomics approaches, showing that GAN methods have great potential for augmentation of the original dataset.