# Applying Deep Learning to Medical Imaging: A Review
## 2 Deep Learning Techniques in Medical Imaging
### Limitations and Challenges  
Despite the successes and potential of deep learning techniques in medical imaging, several common limitations and challenges need to be addressed. These challenges span across convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Shown in Table 1, one primary challenge is the lack of interpretability in deep learning models. CNNs, RNNs, and GANs often act as \"black boxes,\" making it difficult to understand the underlying decision-making processes. This lack of interpretability hinders their adoption in clinical practice, where explainability is crucial. Another challenge lies in the robustness and security of deep learning models. CNNs are susceptible to adversarial examples, which are carefully crafted inputs designed to deceive the model into making incorrect predictions. Adversarial attacks raise concerns about the reliability and trustworthiness of deep learning models in medical imaging applications. Furthermore, deep learning techniques, including CNNs, RNNs, and GANs, require large amounts of annotated data for training. Acquiring labeled medical imaging datasets can be time-consuming, expensive, and sometimes limited in size. Overcoming the challenge of data scarcity and finding efficient ways to leverage unlabeled data, such as unsupervised or semisupervised learning, is essential for the broader adoption of deep learning in medical imaging. Additionally, both RNNs and GANs face specific challenges. RNNs suffer from the vanishing and exploding gradient problem when training deep networks, making it difficult to learn long-term dependencies in sequences. The computational complexity of RNNs is also a concern, especially when dealing with long sequences or large-scale datasets. For GANs, the mode collapse problem is a significant challenge, as it can lead to limited variety and suboptimal results in tasks such as data augmentation and image synthesis. Training GANs can be challenging due to unstable dynamics and convergence issues. Ensuring the quality and reliability of generated images is crucial for their safe and effective use in medical imaging applications. Addressing these limitations and challenges will enhance the interpretability, robustness, scalability, and applicability of deep learning techniques in medical imaging.  
\begin{table}
\begin{tabular}{c c c} \hline
**Techniques** & **Limitations** & **Challenges** \\ \hline \hline CNNs & Lack of interpretability; Requires large amounts of annotated training data & \"Black box\" decision making: Susceptible to adversarial examples \\ \hline RNNs & Vanishing and exploding gradient problem; High computational complexity & Interpretability issues; Difficulty handling long sequences or large-scale datasets \\ \hline GANs & Mode collapse problem; Difficulty in training & \"Black box\" decision making Ensuring the quality and reliability of generated images \\ \hline \hline \end{tabular}
\end{table}
Table 1: Limitations and challenges of different DL techniques.  
Figure 4: A sample illustration of a GAN architecture for medical imaging. The figure shows the generator and discriminator networks, their respective inputs and outputs, and the interaction between the two networks during the training process.