# MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation

## TL;DR

MedSegNet10 is a public repository for medical image segmentation using split-federated learning, preserving data privacy and enabling collaborative training.

## Contribution

The paper introduces MedSegNet10, a repository with pre-trained models for split-federated medical image segmentation.

## Key findings

- MedSegNet10 includes ten pre-trained architectures optimized for various medical image types.
- The repository enables collaborative training without centralizing raw data or labels.
- It supports privacy-preserving medical image segmentation for multiple clinical applications.

## Abstract

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. MedSegNet10 implements SplitFed versions of ten established segmentation architectures, enabling collaborative training without centralizing raw data and labels, reducing the computational load required at client sites. This repository supports researchers, practitioners, trainees, and data scientists, aiming to advance medical image segmentation while maintaining patient data privacy.