Simulating Federated Transfer Learning for Lung Segmentation using Modified UNet Model

• Published in: Procedia computer science Vol. 218; pp. 1485 - 1496
• Main Authors: Ambesange, Sateesh, Annappa, B, Koolagudi, Shashidhar G
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2023; The Author(s). Published by Elsevier B.V
• Subjects: data privacy; Federated Learning; Federated Transfer Learning; Lung image segmentation; MRI image segmentation; Transfer Learning; U-net Architecture; X-ray Image segmentation; Transfer Learning; X-ray Image segmentation; Lung image segmentation; Federated Learning; data privacy; U-net Architecture; Federated Transfer Learning; MRI image segmentation
• DOI: 10.1016/j.procs.2023.01.127
• ISSN: 1877-0509

Lung segmentation helps doctors in analyzing and diagnosing lung diseases effectively. Covid -19 pandemic highlighted the need for such artificial intelligence (AI) model to segment Lung X-ray images and diagnose patient covid conditions, in a short time, which was not possible due to huge number of patient influx at hospitals with the limited radiologist to diagnose based on test report in short time. AI models developed to assist doctors to diagnose faster, faces another challenge of data privacy. Such AI Models, for better performance, need huge data collected from multiple hospitals/diagnostic centres across the globe into single place to train the AI models. Federated Learning (FL) framework, using transfer learning approach addresses these concerns as FL framework doesn't need data to be shared to outside hospital ecosystem, as AI model get trained on local system and AI model get trained on distributed data. FL with Transfer learning doesn't need the parallel training of the model at all participants nodes like other FL. Paper simulates Federated Transfer learning for Image segmentation using transfer learning technique with few participating nodes and each nodes having different size dataset. The proposed method also leverages other healthcare data available at local system to train the proposed model to overcome lack of more data. Paper uses pre-trained weights of U-net Segmentation Model trained for MRI image segmentation to lung segmentation model. Paper demonstrates using such similar healthcare data available at local system helps improving the performance of the model. The paper uses Explainable AI approach to explain the result. Using above three techniques, Lung segmentation AI model gets near perfect segmentation accuracy.