MR Görüntülerinde Federasyonlu Öğrenme Kullanılarak Hepatosit Karsinomu - HCC Karaciğer Kanseri Tanısı

Yıl 2025, Cilt: 40 Sayı: 3, 531 - 544, 26.09.2025

Burak Uzdur , Erkut Tekeli , Turgay İbrikçi , Harun Ur Rashid , Geetha Ramachandran

Öz

Son yıllarda, Federasyonlu Öğrenme, veri gizliliğini korurken merkezi olmayan veri kaynakları arasında makine öğrenimi modellerini eğitmek için güçlü bir paradigma olarak ortaya çıkmıştır. Bu çalışma, hepatosellüler karsinom vakalarının kontrastla geliştirilmiş görüntülerini sağlayan ATLAS veri setinden elde edilen Manyetik Rezonans Görüntüleme kullanılarak karaciğer tümörlerinin sınıflandırılması için bir Federasyonlu Öğrenme çerçevesi önermektedir. Federasyonlu ortamda Evrişimli Sinir Ağı, EfficientNet, MobileNetV3, ResNet50 ve VGG16 mimarileri kullanılarak karşılaştırmalı bir değerlendirme yapılmıştır. Bu modeller arasında, EfficientNet tabanlı Federasyonlu Öğrenme uygulaması, %93,75'lik bir doğruluk ve %99,19'luk bir ROC-AUC puanına ulaşarak üstün bir performans elde etmiştir. Sonuçlar, federasyonlu yaklaşımların hasta verilerinin gizliliğini sağlarken merkezi öğrenmeye benzer performans seviyelerine ulaşabileceğini göstermektedir. Bu çalışma, Federasyonlu Öğrenmenin hassas tıbbi görüntüleme görevlerinde uygulanabilirliğini vurgulamakta ve gizliliği koruyan işbirlikçi model geliştirme potansiyelini vurgulamaktadır. Gelecekteki çalışmalar, heterojen klinik ortamlarda gerçek dünya dağıtımını ve ölçeklenebilirliğini araştırabilir.

Anahtar Kelimeler

Federasyonlu Öğrenme , FedAvg , Karaciğer Tümörü Sınıflandırması , Evrişimli Sinir Ağları , MRI

Diagnosis of Hepatocellular Carcinoma - HCC Liver Cancer Using Federated Learning on MR Images

Öz

In recent years, Federated Learning has emerged as a powerful paradigm for training machine learning models across decentralized data sources while preserving data privacy. This study proposes a Federated Learning framework for the classification of liver tumors using Magnetic Resonance Imaging obtained from the ATLAS dataset, which provides contrast-enhanced images of hepatocellular carcinoma cases. A comparative evaluation was performed utilizing Convolutional Neural Network, EfficientNet, MobileNetV3, ResNet50, and VGG16 architectures within the federated environment. Among these models, the Federated Learning implementation based on EfficientNet achieved superior performance, reaching an accuracy of 93.75% and a ROC-AUC score of 99.19%. The results demonstrate that federated approaches can attain performance levels comparable to centralized learning while ensuring patient data confidentiality. This study highlights the applicability of Federated Learning in sensitive medical imaging tasks and emphasizes its potential for privacy-preserving collaborative model development. Future work may explore real-world deployment and scalability across heterogeneous clinical settings.

Anahtar Kelimeler

Federated Learning , FedAvg , Liver Tumor Classification , Convolutional Neural Networks , MRI

Kaynakça

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