# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 9 Challenges and Future Directions
### Explainable AI  
As these systems become more complex, there will be an increased emphasis on developing explainable AI models. This will be crucial for clinical adoption, allowing pathologists to understand and trust the decision-making process of AI systems. Grad-CAM, SHAP, and LIME are three popular explainable AI (XAI) techniques used to interpret and understand the decisions made by machine learning models, particularly in image classification tasks. Grad-CAM is a visualization technique specifically designed for CNNs. It uses the gradients of a target concept flowing into the final convolutional layer to generate a coarse localization map of important regions in the input image. Key features of Grad-CAM include that it is (i) applicable to a wide range of CNN architectures without requiring architectural changes, (ii) provides spatial information about important regions in the input image [187], (iii) faster to compute compared to some other XAI methods [187], and (iv) useful for visualizing the contribution of each convolutional layer to the final prediction. Also, SHAP (Shapley Additive exPlanations) is based on game theory concepts, particularly Shapley values, to explain the output of any machine learning model. It offers several advantages that can be applied to various types of machine learning models, not just CNNs, providing detailed feature attribution, showing how each input feature contributes to the model's output [187], offering both local (per-sample) and global (model-wide) explanations, and has desirable mathematical properties, including consistency and efficiency. In addition, LIME (Local Interpretable Model-agnostic Explanations) is another model-agnostic explanation technique that aims to explain individual predictions. Its key characteristics include that it creates locally faithful explanations by approximating the model around a specific prediction, can be applied to various types of data and models, and uses an interpretable model (e.g., ridge regression) to approximate the complex model's behavior locally. When comparing these methods, Grad-CAM is particularly useful for CNN-based image classification tasks, providing spatially oriented explanations [187]. SHAP offers more detailed feature attribution and can be applied to a wider range of models but may be computationally more intensive [187]. LIME provides local explanations that are easy to interpret but may be less consistent across similar inputs compared to SHAP. The choice between these methods depends on the specific application, model type, and the level of detail required in the explanations. In some cases, using multiple methods can provide complementary insights into model behavior. Future research is likely to focus on developing more robust and explainable AI models, as well as validating their performance in real-world clinical settings. The integration of AI with other emerging technologies like multiphoton microscopy may lead to more powerful multi-dimensional diagnostic approaches with the following requirements:  
* Ensuring model generalizability across different laboratories and staining protocols. * Addressing potential biases in training data. * Integrating AI tools into existing clinical workflows. * Maintaining interpretability of AI-assisted diagnoses. Computer vision and AI techniques have shown great potential for enhancing pathological image classification and analysis [175; 180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 190; 191]. As these technologies continue to advance, they are poised to become valuable tools supporting pathologists in making more accurate and efficient diagnoses. Future research will need to address several key challenges, including (i) improving model generalization across different imaging modalities and patient populations, (ii) developing more efficient methods for handling large-scale WSIs, (iii) addressing issues of data privacy and security in medical imaging, and (iv) enhancing the interpretability of deep learning models for clinical use.