The Influence of Artificial Intelligence Algorithm Optimization on the Accuracy of Pathological Diagnosis in Electronic Information Engineering

Aiming at the complexity and diversity of pathological image analysis, this paper proposes an innovative pathological image segmentation framework based on graph network, aiming to improve the accuracy of diagnosis through advanced algorithm optimization. The framework is designed with two modes: fully supervised graph network and weakly supervised graph network to flexibly adapt to data sets with different amounts of annotations. The fully supervised model is trained with a large amount of precisely labeled data and can capture subtle pathological features, which is suitable for situations where high diagnostic accuracy is required. The weakly supervised mode can realize effective image segmentation by learning the global structure and local texture of the image with limited annotation information. It can be used in resource-constrained or mass screening Settings. The experimental results show that the proposed framework can significantly improve the accuracy of pathological image segmentation and effectively assist doctors in disease diagnosis, regardless of the full supervision or weak supervision mode. In addition, the framework shows good generalization ability and can be migrated to different pathologic types and image sources, showing broad application prospects.