Conference Papers
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Item Abdominal Multi-Organ Segmentation Using Federated Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Yadav, G.; Annappa, B.; Sachin, D.N.Multi-organ segmentation refers to precisely de-lineating and identifying multiple organs or structures within medical images, such as Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI), to outline boundaries and regions for each organ accurately. Medical imaging is crucial to comprehending and diagnosing a wide range of illnesses for which accurate multi-organ image segmentation is often required for successful analysis. Due to the delicate nature of medical data, traditional methods for multi-organ segmentation include centralizing data, which presents serious privacy problems. This centralized training strategy impedes innovation and collaborative efforts in healthcare by raising worries about patient confidentiality, data security, and reg-ulatory compliance. The development of deep learning-based image segmentation algorithms has been hindered by the lack of fully annotated datasets, and this issue is exacerbated in multi-organ segmentation. Federated Learning (FL) addresses privacy concerns in multi-organ segmentation by enabling model training across decentralized institutions without sharing raw data. Our proposed FL-based model for CT scans ensures data privacy while achieving accurate multi-organ segmentation. By leveraging FL techniques, this paper collaboratively trains segmentation models on local datasets held by distinct medical institutions. The expected outcomes encompass achieving high Dice Similarity Coefficient (DSC) metrics and validating the efficacy of the proposed FL approach in attaining precise and accurate segmentation across diverse medical imaging datasets. © 2024 IEEE.Item Enhancing Healthcare AI with Cross-Silo Personalized Federated Learning on Naturally Split Heterogeneous Data(Institute of Electrical and Electronics Engineers Inc., 2024) Mukeshbhai, A.N.; Annappa, B.; Sachin, D.N.The potential of Artificial Intelligence (AI) in health-care is unavoidable. However, its success depends on the availabil-ity of large, high-quality datasets. Because of data heterogeneity across institutions and privacy concerns, traditional centralized Machine Learning (ML) approaches often face difficulties in this field. Federated Learning (FL) allows collaborative model training without requiring the transfer of sensitive patient data from the original institution. Recent research in FL within the healthcare domain has predominantly relied on centralized datasets, which do not represent real-time data heterogeneity and made assumptions by random data splitting to different medical client institutions. Additionally, it may be challenging for a single global model to encompass the diverse characteristics of various healthcare settings accurately. This paper examines the application of Personalized Federated Learning (PFL) in realistic cross-silo healthcare scenarios with federated natural split datasets in different medical client institutions. This paper discusses the experiments conducted on brain segmentation, survival prediction, melanoma classification, and heart disease di-agnosis. Our experiments show that the proposed PFL techniques consistently improve local model performance over standard FL strategies by up to 10% in different medical use cases. © 2024 IEEE.