# Generative Adversarial Networks in Retinal Image Classification
## 5 Discussion  
As already mentioned in the introduction section, GANs have shown significant promise in a plethora of fields, including the biomedical context. However, similarly to any newly proposed method, GANs come with their own set of strengths and weaknesses when applied to biomedical applications. In this section, we discuss the strong points and the weaknesses of GAN adoption related to the biomedical context and we provide a table aimed at summarizing the state of the art in the adoption of GANs in the biomedical context with particular regard to the papers related to retinal images. In the following we itemize the strong points related to GAN adoption in the biomedical context:  
* Data Augmentation: GANs can generate synthetic data that closely resemble real biomedical data. This is particularly useful when the available dataset is small or lacks diversity. GAN-generated data can be used to augment the training data, leading to better model generalization. * Image Synthesis: GANs excel at generating high-quality images. In the biomedical field, this can be used for tasks like generating medical images (e.g., MRI and CT scans) with different contrasts, resolutions, or pathologies. It can aid in medical image analysis and training image-based models. * Drug Discovery: GANs can generate molecular structures with desired properties. They can be used for drug discovery by generating novel chemical structures that match specific criteria, potentially accelerating the drug development process. * Data Privacy: GANs can generate synthetic data that preserve the statistical properties of the original data while ensuring privacy. This can help in sharing medical data without revealing sensitive patient information. * Noise Reduction: GANs can be employed to denoise biomedical images, which is crucial for accurate medical diagnosis. They can help in improving the quality of noisy or low-resolution images. Below we itemize the weaknesses related to GAN adoption in the biomedical context:  
* Data Quality: GANs are highly sensitive to input data quality. If the initial dataset contains errors, biases, or inaccuracies, the generated data may inherit these issues, potentially leading to unreliable results. * Mode Collapse: GANs can suffer from mode collapse, where the generator produces a limited variety of outputs, failing to capture the full diversity of the underlying data distribution. This can hinder the effectiveness of the model. * Training Challenges: GANs can be challenging to train and require careful tuning of hyperparameters. Training instability, vanishing gradients, and mode dropping are common issues that can make training difficult. * Ethical Concerns: In the biomedical field, generating synthetic medical images that are indistinguishable from real ones raises ethical concerns. There is a risk of inadvertently creating misleading or potentially harmful information. * Interpretability: GANs are often considered \"black box\" models, making it challenging to interpret how they generate certain outputs. This lack of interpretability can be problematic in medical applications where understanding the decision-making process is crucial. * Generalization: GAN-generated data might not perfectly mimic real-world data distribution, leading to potential challenges in generalizing the models to real-world scenarios. Careful validation and testing are required to ensure real-world applicability. In summary, GANs offer valuable contributions in the biomedical context, particularly in data augmentation, image synthesis, and drug discovery. In Table 5 we provide a table with a state-of-the-art comparison between different research papers combining GAN and biomedical images in terms of the kind of GAN exploited (i.e., GAN Type column), the contributions of the paper, and the obtained F-Measure.