From a638c101fe7ddbf9adab08b4db6329369b0be3ba Mon Sep 17 00:00:00 2001
From: Thibault Wang <35058075+thibault-wch@users.noreply.github.com>
Date: Sat, 27 Apr 2024 11:19:45 +0800
Subject: [PATCH 1/2] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 5ec3ed2..dbbf5ab 100755
--- a/README.md
+++ b/README.md
@@ -2,6 +2,8 @@
 
 **[MedIA 2024]** This is a code implementation of the **joint learning framework** proposed in the manuscript "**Joint learning Framework of cross-modal synthesis and diagnosis for Alzheimer's disease by mining underlying shared modality information**".[[Paper]](https://doi.org/10.1016/j.media.2023.103032) [[Supp.]](./readme_files/main_supp.pdf)
 
+🌟🌟🌟 We also plan to open a **unified codebase for 3D cross-modality medical synthesis** in [[code]](https://github.com/thibault-wch/A-Unified-3D-Cross-Modality-Synthesis-Codebase), including CNN-based, GAN-based, and Diffusion-based SOTA generative methods.
+
 ## Introduction
 Among various neuroimaging modalities used to diagnose AD, functional positron emission tomography (**PET**) has higher sensitivity than structural magnetic resonance imaging (**MRI**), but it is also **costlier and often not available** in many hospitals.
 How to **leverage massive unpaired unlabeled PET to improve the diagnosis performance of AD from MRI** becomes rather important. 

From 857876b3ec0f2afd55489a6e2b5e85460a69a179 Mon Sep 17 00:00:00 2001
From: Thibault Wang <35058075+thibault-wch@users.noreply.github.com>
Date: Fri, 10 May 2024 12:22:10 +0800
Subject: [PATCH 2/2] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index dbbf5ab..4013378 100755
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 **[MedIA 2024]** This is a code implementation of the **joint learning framework** proposed in the manuscript "**Joint learning Framework of cross-modal synthesis and diagnosis for Alzheimer's disease by mining underlying shared modality information**".[[Paper]](https://doi.org/10.1016/j.media.2023.103032) [[Supp.]](./readme_files/main_supp.pdf)
 
-🌟🌟🌟 We also plan to open a **unified codebase for 3D cross-modality medical synthesis** in [[code]](https://github.com/thibault-wch/A-Unified-3D-Cross-Modality-Synthesis-Codebase), including CNN-based, GAN-based, and Diffusion-based SOTA generative methods.
+🌟🌟🌟 We also plan to open a **unified codebase for 3D cross-modality medical synthesis** in [[code]](https://github.com/thibault-wch/A-Unified-3D-Cross-Modality-Synthesis-Codebase), including **updated multi-thread preprocessing steps for MRI and PET**, **a series of generated methods** (CNN-based, GAN-based, and Diffusion-based), and **full evaluation pipelines for 3D images**.
 
 ## Introduction
 Among various neuroimaging modalities used to diagnose AD, functional positron emission tomography (**PET**) has higher sensitivity than structural magnetic resonance imaging (**MRI**), but it is also **costlier and often not available** in many hospitals.