A semi-supervised federated learning approach leveraging pseudo-labeling for Knee Osteoarthritis severity detection

Abstract

Within medical image analysis, appropriately classifying the extent of knee osteoarthritis is a significant obstacle, made more difficult by the scarcity of annotated data and strict privacy rules. Conventional approaches are hindered by the exorbitant expenses, limited availability of annotated datasets, as well as issues over the confidentiality of patient data. To overcome these challenges, we propose a method which is a Federated Learning Framework that utilizes pseudo-labeling we are calling it PLFL. Our innovative approach avoids the cost of human annotation and guarantees patient confidentiality through Federated Learning while reducing the dangers linked to adversarial assaults and annotation mistakes. Our proposed method works under the assumption that the server is the only custodian of gold label data, while the client side does not have any label data. The server utilizes gold-labeled data to train the global model and subsequently applies the federated learning approach. Clients add labels to unlabeled data by picking labels that meet or exceed a minimal threshold level of confidence in the prediction. Once data on the client side reaches the specified confidence score, it is added to the client’s dataset. Upon receiving the labeled data, the client initiates the training process and sends the weight of the local model. Subsequently, the server aggregates the weights of each model using the FedAvg technique. The thorough assessment of our system, in comparison to the standard client-server-based Federated Learning approach (CSFL) and FixMatchbased semi-supervised Federated Learning (FSSFL) approach, clearly shows significant performance improvements. Our framework PLFL showed superior performance compared to other explained techniques, with consistent accuracy, weighted average precision, recall, and an F1-score of 0.88. Significantly, it outperforms both CSFL and FSSFL Frameworks, significantly enhancing model performance and efficiency. The proposed framework achieves an accuracy of 93.07% for the healthy class, 64.00% for the moderate class, and 100% for the severe class. Furthermore, our system has exceptional prediction precision, especially in detecting moderate and severe instances of osteoarthritis, surpassing rival frameworks. This is seen in the notable progress in accurately forecasting moderate and severe categories, highlighting the effectiveness of our method. The pseudo-labeling-based framework had the shortest duration for label generation and model training, 3.2 times shorter than the best-performing model of the traditional Federated Learning Framework (CSFL) and 1.7 times lower than the best-performing model of the FixMatch-Based Federated Learning Framework (FSSFL). This thesis proposes an innovative investigation into identifying knee osteoarthritis severity, the first instance of applying semi-supervised and federated learning approaches in this field. Our goal is to stimulate progress in medical image analysis by using our innovative technique, resulting in more precise diagnoses and better patient outcomes.