MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916814223572992 |
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| author | Shiranthika, Chamani Kafshgari, Zahra Hafezi Hadizadeh, Hadi Saeedi, Parvaneh |
| author_facet | Shiranthika, Chamani Kafshgari, Zahra Hafezi Hadizadeh, Hadi Saeedi, Parvaneh |
| contents | Machine Learning (ML) and Deep Learning (DL) have shown significant promise in healthcare, particularly in medical image segmentation, which is crucial for accurate disease diagnosis and treatment planning. Despite their potential, challenges such as data privacy concerns, limited annotated data, and inadequate training data persist. Decentralized learning approaches such as federated learning (FL), split learning (SL), and split federated learning (SplitFed/SFL) address these issues effectively. This paper introduces "MedSegNet10," a publicly accessible repository designed for medical image segmentation using split-federated learning. MedSegNet10 provides a collection of pre-trained neural network architectures optimized for various medical image types, including microscopic images of human blastocysts, dermatoscopic images of skin lesions, and endoscopic images of lesions, polyps, and ulcers, with applications extending beyond these examples. By leveraging SplitFed's benefits, MedSegNet10 allows collaborative training on privately stored, horizontally split data, ensuring privacy and integrity. This repository supports researchers, practitioners, trainees, and data scientists, aiming to advance medical image segmentation while maintaining patient data privacy. The repository is available at: https://vault.sfu.ca/index.php/s/ryhf6t12O0sobuX (password upon request to the authors). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20830 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation Shiranthika, Chamani Kafshgari, Zahra Hafezi Hadizadeh, Hadi Saeedi, Parvaneh Image and Video Processing Computer Vision and Pattern Recognition Machine Learning (ML) and Deep Learning (DL) have shown significant promise in healthcare, particularly in medical image segmentation, which is crucial for accurate disease diagnosis and treatment planning. Despite their potential, challenges such as data privacy concerns, limited annotated data, and inadequate training data persist. Decentralized learning approaches such as federated learning (FL), split learning (SL), and split federated learning (SplitFed/SFL) address these issues effectively. This paper introduces "MedSegNet10," a publicly accessible repository designed for medical image segmentation using split-federated learning. MedSegNet10 provides a collection of pre-trained neural network architectures optimized for various medical image types, including microscopic images of human blastocysts, dermatoscopic images of skin lesions, and endoscopic images of lesions, polyps, and ulcers, with applications extending beyond these examples. By leveraging SplitFed's benefits, MedSegNet10 allows collaborative training on privately stored, horizontally split data, ensuring privacy and integrity. This repository supports researchers, practitioners, trainees, and data scientists, aiming to advance medical image segmentation while maintaining patient data privacy. The repository is available at: https://vault.sfu.ca/index.php/s/ryhf6t12O0sobuX (password upon request to the authors). |
| title | MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.20830 |