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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Neeshu Chaudhary
Computer Science and Engineering, Parul University, Gujarat, India
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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FedBrain-3DMRI: Federated Self-Supervised Learning for 3D Brain Tumor Segmentation using SCAFFOLD Algorithm Neeshu Chaudhary; Chintan Thacker
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1596

Abstract

Brain tumor segmentation is the most important way to separate tumor areas from healthy brain tissue in medical imaging. This is necessary for making an accurate diagnosis and planning treatment. But building strong deep learning models is often hard because there isn't much labeled medical data available, and strict privacy rules stop data from being shared in one place. Federated Learning (FL) helps keep patient data private by keeping it local, but its performance often drops when data from different hospitals have big differences in quality, imaging protocols, and distribution. Our research seeks to create a privacy-preserving federated learning framework that adeptly manages significant data heterogeneity while ensuring high segmentation accuracy across various institutions. We propose a new two-stage FL framework that allows multiple institutions to work together while keeping their privacy and effectively dealing with complicated non-IID data distributions. To start, we use a Federated Masked Autoencoder (MAE) for self-supervised pre-training. This lets the model learn strong anatomical features from unlabeled MRI scans. Second, the model is carefully fine-tuned using an Attention ResUNet3D architecture to get very accurate tumor segmentation. We use the SCAFFOLD optimization algorithm to keep training stable across all clients, even when the scanner varies from site to site, thereby directly addressing client drift. We also use strategic foreground-biased sampling and Test-Time Augmentation (TTA) techniques to greatly improve segmentation accuracy in difficult, uneven tumor sub-regions. We ran extensive experiments on the BraTS 2024 dataset in simulated federated settings with 10, 50, and 100 different clients. The Dice coefficients we got were 0.826, 0.824, and 0.818, which demonstrate strong performance. In the end, these strong results show that the suggested method works well on a larger scale and can be used in a clinical setting.
ProtoSurv-X: Explainable Brain Tumor Survival Prediction Neeshu Chaudhary; Saurabh Shah; Chintan Thacker
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1899

Abstract

Accurate survival prediction for patients with high-grade glioma is important for prognostic assessment and personalized treatment planning; however, substantial intratumoral heterogeneity, complex multimodal MRI patterns, and limited interpretability challenge existing deep learning approaches. This study aims to develop ProtoSurv-X, an explainable and uncertainty-aware framework for MRI-based glioma survival prediction that integrates probabilistic tumor habitat modeling, prototype-guided learning, evidential prediction, and evidence-grounded clinical explanations. A unified cohort was constructed from the BraTS 2019 and BraTS 2020 datasets by removing duplicate subjects, resulting in 369 unique subjects, including 118 gross total resection patients with complete survival annotations. The proposed framework uses diffusion-enhanced SwinUNETR for tumor segmentation, probabilistic habitat construction, and fusion of radiomic, deep imaging, habitat, and age-related features. Prototype learning and survival-aware contrastive learning generate prognostic representations, while Evidential Deep Learning estimates risk and predictive uncertainty alongside continuous survival regression. For explanation generation, seven open-source large language models were benchmarked using structured model evidence, and a Meta-Llama-3.1-8B-Instruct model was fine-tuned using QLoRA. In five-fold cross-validation on the unified survival cohort, ProtoSurv-X achieved a mean absolute error of 145.2 ± 6.5 days, an RMSE of 189.4 ± 8.3 days, a C-index of 0.745 ± 0.008, and an integrated Brier score of 0.124. The segmentation module achieved Dice scores of 91.24%, 87.38%, and 81.76% for whole tumor, tumor core, and enhancing tumor, respectively, on BraTS 2019. The fine-tuned explanation model obtained a BERTScore of 0.915 and a hallucination rate of 2.6%. These findings indicate that ProtoSurv-X offers a unified computational framework for accurate, uncertainty-aware, and evidence-grounded glioma prognostic modeling, while further clinical and multicenter validation remains necessary.
Co-Authors Chintan Thacker Chintan Thacker Saurabh Shah