From 07f404b176d349e478071dd95b1477c3c81cc276 Mon Sep 17 00:00:00 2001 From: mmasoud1 Date: Wed, 8 Jan 2025 09:55:27 -0500 Subject: [PATCH] Fix paper --- paper/paper.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index b4ee518..540ac0b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -44,7 +44,7 @@ MeshFL provides an easy-to-use yet robust environment for researchers and clinic MeshFL leverages NVFlare to implement federated learning workflows, allowing local sites to independently train the MeshNet model on their data and exchange model updates with a central server as shown in \autoref{fig:MeshFL-Seq-Diagram}. - +![MeshFL Sequence Diagram.](MeshFL-Seq-Diagram.png){ width=80% } MeshFL key features include: - **Data Preprocessing:** Automated partitioning of MRI scans into training, validation, and testing sets. @@ -64,6 +64,7 @@ The performance of MeshFL was validated using the Mindboggle dataset [@mindboggl Results demonstrated that MeshFL achieved Dice scores of ~0.92 for training and ~0.9 for validation with robust performance comparable to centralized training \autoref{fig:MeshFL-Performance}. +![MeshFL Training Performance.](MeshFL-Performanc.png){ width=80% } # Code Availability