# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 4 Architectures, Features, and Advantages of CNNs
They can capture hierarchical patterns in text data, mirroring human language comprehension. In speech recognition and sound classification, CNNs excel at processing time-series audio data and extracting relevant features. This makes them valuable for applications like voice assistants and environmental sound monitoring. CNNs are increasingly used in analyzing genetic data and predicting protein structures [153, 154, 155]. Their ability to process large, complex datasets is transforming personalized medicine and genomics research. By leveraging these advantages, CNNs have become a cornerstone of modern deep learning, driving advancements in various fields and enabling more sophisticated AI applications [41]. However, CNNs typically require large amounts of labeled data for training and can be computationally intensive, often necessitating specialized hardware like Graphics Processing Units (GPUs) for optimal performance [156]. As deep learning continues to evolve, CNNs remain a cornerstone in computer vision and image processing, with ongoing research focused on improving their efficiency, interpretability, and applicability to new domains [46]. The Datasets Referenced in Studies on the Diagnostic Classification of Pathological Images Using Computer Vision, Including Both Publicly Available and Proprietary Datasets  
As for publicly available datasets, the CPIA dataset combines 103 open-source datasets, including standardized WSIs and regions of interest (ROIs), covering over 48 organs/tissues and about 100 diseases [157]. Datasets, such as The Cancer Genome Atlas (TCGA), Camelyon, and others, are commonly used in pathology research. TCGA, for instance, provides over 1.2 petabytes of cancer-related pathology slides [158]. Synthetic datasets like SNOW (Synthetic Nuclei and Annotation Wizard) are also publicly available, providing annotated synthetic pathological images for tasks like nuclei segmentation [159]. Platforms like Kaggle and HuggingFace host medical imaging datasets, which have become increasingly accessible to the public [160]. In addition, as for proprietary datasets, some foundation models are trained on proprietary datasets collected from specific institutions. For example, the Virchow model utilizes a dataset of over 1.4 million WSIs from Memorial Sloan Kettering Cancer Center. Many studies still rely on proprietary datasets due to the limited availability of public WSIs with detailed annotations [158, 161]. Thus, while there is a grow ing trend toward using publicly available datasets, proprietary datasets remain significant in computational pathology research due to their scale and specificity. Studies on diagnostic classification of pathological images using computer vision have employed various strategies to curate datasets and ensure their quality and representativeness. There are some dataset curation approaches, such as synthetic data generation, dataset distillation, and automated curation. Synthetic datasets, such as the SNOW dataset, have been created to address the challenges of privacy concerns and annotation burdens in pathology. These datasets are generated using standardized workflows and automated tools for image generation and annotation, ensuring data diversity and cost-effectiveness. For example, SNOW includes 20,000 image tiles with over 1.4 million annotated nuclei, enabling both supervised and semi-supervised training scenarios [159]. Techniques like Histo-DD compress large-scale datasets into condensed synthetic samples optimized for downstream tasks. These methods integrate stain normalization and model augmentation to enhance compatibility with histopathology images, which often exhibit high variability in color and texture. The synthetic samples generated are architecture-agnostic, reducing training efforts while preserving discriminative information [162]. Deep learning-based methods have been used to automatically curate large-scale datasets, sorting and labeling WSIs efficiently.