# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 4 Architectures, Features, and Advantages of CNNs
Performance is evaluated across various pre-trained and randomly initialized networks to demonstrate the methodology's independence from specific model characteristics [170]. CNN models may lead to excessive smoothing of images under small dose-reduction conditions in CT scans [167]. Techniques like saliency maps enhance the interpretability of CNN models, especially in complex tasks like [PERSON]'s disease classification [169]. While many approaches are developed for specific imaging modalities or diseases, efforts are being made to create adaptable frameworks that can be applied to various samples, microscope modalities, and cell measurements [168]. Thus, the quantitative evaluation of CNN models for medical image analysis is a rapidly evolving field, with researchers continuously developing new metrics and methodologies to assess and improve model performance across different imaging modalities and diseases. State-of-the-art methods for the diagnostic classification of pathological images using computer vision have made significant advancements in recent years. These methods primarily leverage deep learning techniques, particularly CNNs and ViTs, to achieve high accuracy in various diagnostic tasks [106; 171; 172]. CNNs remain a cornerstone in pathological image analysis. Pre-trained models like DenseNet-161 and ResNet-50 have shown excellent performance in classifying digital histopathology patches into corresponding WSIs [172]. These models outperform previous state-of-the-art methods across all performance metrics for classifying digital pathology patches into 24 categories. Recent studies have demonstrated the effectiveness of ViTs in pathological image analysis. Hierarchical ViT architectures have been successfully employed for various diagnostic tasks, including the identification of malignant cells, prediction of histological grades, glioma-type classification, according to the 2021 WHO classification system, and molecular marker prediction [106; 172]. There are some advanced techniques, such as transfer learning, weakly supervised learning, and few-shot learning. Transfer learning has proven to be a powerful technique in pathological image classification. By utilizing pre-trained models on large datasets and fine-tuning them for specific pathological tasks, researchers have achieved high accuracy even with limited labeled data [173]. Also, weakly supervised CNNs have shown promise in diagnosing specific conditions like intracranial germinomas, oligodendrogliomas, and low-grade astrocytomas from H&E-stained and intraoperative frozen sections. These models have demonstrated the ability to improve the diagnostic accuracy of pathologists by up to 40% [106; 172]. Further, in scenarios with limited labeled data, few-shot learning techniques have made significant progress. The best methods have achieved accuraciesexceeding 70%, 80%, and 85% in 5-way 1-shot, 5-way 5-shot, and 5-way 10-shot cases, respectively [174]. As emerging approaches, there are hierarchical classification or muti-modal learning. Novel approaches like PathTree introduce the concept of hierarchical pathological image classification. This method represents the multi-classification of diseases as a binary tree structure, using professional pathological text descriptions to guide the aggregation of hierarchical multiple representations [175]. In addition, integrating textual information with image data has shown promise in improving classification accuracy. Methods like PathTree use slide-text similarity and tree-specific losses to enhance the association between textual descriptions and slide images [175]. These state-of-the-art methods are significantly improving the accuracy and efficiency of pathological image analysis, potentially revolutionizing clinical diagnosis and treatment decision-making in the field of digital pathology.