A novel federated deep learning scheme for glioma and its subtype classification - Ali, Muhaddisa Barat; Gu, Irene Yu-Hua; Berger, Mitchel S.; Jakola, Asgeir Store (Frontiers in Neuroscience, 2023)
Background: Deep learning (DL) requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Method: We propose a novel 3D FL scheme for glioma and its molecular subtype classification. A slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn (1), with the key differences on using focal loss function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, it examines whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Results: : Experiments were done on 2 case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56% for IDH subtypes and 89.28%, 90.72% for glioma LGG/HGG all averaged on 5 runs). Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (-1.17%, -0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85% in case A), focal loss function (1.66%, 3.25% in case A, 1.19%, 1.85% in case B), 3D post-processing (2.11%, 2.23% in case A and 1.81%, 2.39% in case B) and EtFedDyn over FedAvg classifier (1.05%, 1.55% in case A and 1.23%, 1.81% in case B) with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme. Conclusion: The proposed FL scheme is shown to be effective in predicting glioma and its subtype, with great potential of replacing the conventional CL approaches for training deep networks.