ResInvolution: an involution-ResNet fused global spatial relation leveraging model for histopathological image analysis under federated learning environment

Abstract

Accessing image data in the domain of medical image analysis is challenging owing to concerns regarding privacy. Federated Learning is the approach used to get rid of this challenge. With millions of learning parameters, Residual Network (ResNet) is one of the most advanced architectures for classifying medical images. Because of its resource-hungry nature, using this ResNet architecture in the Federated learning framework has an impact on the entire system. This research introduces a novel architecture called Residual Involution (ResInvolution), specifically developed for analyzing histopathological images within a federated learning environment. The architecture utilizes a cutting-edge model, the Involution-ResNet Fused Global Spatial Relation Leveraging model, to enhance the analysis process. This model is impressively lightweight, boasting less than 190,000 parameters. Its efficiency and ease of deployment make it ideal for medical image analysis tasks. By incorporating involution operations into the ResNet framework, it becomes possible to adjust the spatial weighting of features dynamically. The proposed model enables a comprehensive analysis of intricate structures that exceed the capabilities of traditional convolutional networks. This model has been deployed within a federated learning environment, where privacy is prioritized. Also utilize decentralized data sources, thereby eliminating the necessity of centralizing sensitive medical images. This approach ensures strict adherence to medical data privacy regulations while simultaneously leveraging collective insights from multiple institutions. The model has undergone rigorous testing on three distinct datasets: GasHisSDB, NTC-CRC-HE- 100K, AND LC25000. In Federated Learning scenarios, the model achieves accuracies of 91%, 95%, and 99% on these datasets, respectively. However, in the context of federated learning, the accuracies exhibited are 91%, 93%, and 97%, respectively. The model’s effectiveness is evaluated through various performance metrics, including the confusion Matrix, Accuracy, Precision, Recall, F1-Score, Receiver operating Characteristic (ROC) curve, and Area under the ROC Curve (AUC) Score. The results highlight the model’s ability to adapt to various challenges, such as limited data and irregular data distribution, commonly encountered in federated learning environments. ResInvolution sets a revolutionary benchmark in medical image analysis, enhancing the ability to interpret intricate medical images and paving the way for future advancements in scalable, privacy-preserving deep learning technologies.