diff --git a/README.md b/README.md
index 85c55408c..9ab33541c 100644
--- a/README.md
+++ b/README.md
@@ -315,29 +315,29 @@ This tutorial shows how to construct a training workflow of self-supervised lear
 ##### [self_supervised_pretraining_based_finetuning](self_supervised_pretraining/vit_unetr_ssl/ssl_finetune.ipynb)
 This tutorial shows how to utilize pre-trained weights from the self-supervised learning framework where unlabeled data is utilized. This tutorial shows how to train a model of multi-class 3D segmentation using pretrained weights.
 
-#### [Generative Model](./generative)
-##### [3D latent diffusion model](./generative/3d_ldm)
+#### [Generative Model](./generation)
+##### [3D latent diffusion model](./generation/3d_ldm)
 This tutorial shows the use cases of training and validating a 3D Latent Diffusion Model.
 
-##### [2D latent diffusion model](./generative/2d_ldm)
+##### [2D latent diffusion model](./generation/2d_ldm)
 This tutorial shows the use cases of training and validating a 2D Latent Diffusion Model.
 
-##### [Brats 3D latent diffusion model](./3d_ldm/README.md)
+##### [Brats 3D latent diffusion model](./generation/3d_ldm/README.md)
 Example shows the use cases of training and validating a 3D Latent Diffusion Model on Brats 2016&2017 data, expanding on the above notebook.
 
-##### [MAISI 3D latent diffusion model](./maisi/README.md)
+##### [MAISI 3D latent diffusion model](./generation/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 in VAE-GAN for Semantic Image Synthesis on 2D BraTS Data](./generation/spade_gan)
 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)
+##### [Applying Latent Diffusion Models to 2D BraTS Data for Semantic Image Synthesis](./generation/spade_ldm)
 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)
+##### [Diffusion Models for Implicit Image Segmentation Ensembles](./generation/image_to_image_translation)
 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)
+##### [Evaluate Realism and Diversity of the generated images](./generation/realism_diversity_metrics)
 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.
 
 #### [VISTA2D](./vista_2d)