From f72bbeba575fc5237d3fda4c350b9f653c118219 Mon Sep 17 00:00:00 2001
From: YunLiu <55491388+KumoLiu@users.noreply.github.com>
Date: Tue, 3 Sep 2024 17:16:03 +0800
Subject: [PATCH] update readme for generation

Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>
---
 generation/README.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/generation/README.md b/generation/README.md
index 0aa10497c..351d57196 100644
--- a/generation/README.md
+++ b/generation/README.md
@@ -27,3 +27,15 @@ Example shows the use cases of training and validating a 3D Latent Diffusion Mod
 
 ## [MAISI 3D latent diffusion model](./maisi/README.md)
 Example shows the use cases of training and validating Nvidia MAISI (Medical AI for Synthetic Imaging) model, a 3D Latent Diffusion Model that can generate large CT images with paired segmentation masks, variable volume size and voxel size, as well as controllable organ/tumor size.
+
+## [SPADE in VAE-GAN for Semantic Image Synthesis on 2D BraTS Data](./spade_gen/spade_gen.ipynb)
+Example shows the use cases of applying SPADE, a VAE-GAN-based neural network for semantic image synthesis, to a subset of BraTS that was registered to MNI space and resampled to 2mm isotropic space, with segmentations obtained using Geodesic Information Flows (GIF).
+
+## [Applying Latent Diffusion Models to 2D BraTS Data for Semantic Image Synthesis](./spade_ldm/spade_ldm_brats.ipynb)
+Example shows the use cases of applying SPADE normalization to a latent diffusion model, following the methodology by Wang et al., for semantic image synthesis on a subset of BraTS registered to MNI space and resampled to 2mm isotropic space, with segmentations obtained using Geodesic Information Flows (GIF).
+
+## [Diffusion Models for Implicit Image Segmentation Ensembles](./image_to_image_translation/tutorial_segmentation_with_ddpm.ipynb)
+Example shows the use cases of how to use MONAI for 2D segmentation of images using DDPMs. The same structure can also be used for conditional image generation, or image-to-image translation.
+
+## [Evaluate Realism and Diversity of the generated images](./realism_diversity_metrics/realism_diversity_metrics.ipynb)
+Example shows the use cases of using MONAI to evaluate the performance of a generative model by computing metrics such as Frechet Inception Distance (FID) and Maximum Mean Discrepancy (MMD) for assessing realism, as well as MS-SSIM and SSIM for evaluating image diversity.