diff --git a/README.md b/README.md
index f1af10192..6e6f7c88a 100644
--- a/README.md
+++ b/README.md
@@ -157,7 +157,7 @@ This folder provides a simple baseline method for training, validation, and infe
 This notebook demonstrates how to construct a training workflow of UNETR on multi-organ segmentation task using the BTCV challenge dataset.
 ##### [unetr_btcv_segmentation_3d_lightning](./3d_segmentation/unetr_btcv_segmentation_3d_lightning.ipynb)
 This tutorial demonstrates how MONAI can be used in conjunction with [PyTorch Lightning](https://www.pytorchlightning.ai/) framework to construct a training workflow of UNETR on multi-organ segmentation task using the BTCV challenge dataset.
-##### [vista3d](./3d_segmentation/vista3d)
+##### [vista3d](./vista_3d)
 This tutorial showcases the process of fine-tuning VISTA3D on [MSD Spleen dataset](http://medicaldecathlon.com) using MONAI. For an in-depth exploration, please visit the [VISTA](https://github.com/Project-MONAI/VISTA) repository.
 
 #### <ins>**2D registration**</ins>
diff --git a/deepedit/ignite/README.md b/deepedit/ignite/README.md
index 3d7f76672..16a97bf35 100644
--- a/deepedit/ignite/README.md
+++ b/deepedit/ignite/README.md
@@ -92,7 +92,7 @@ python ./train.py
     --export      true
 ```
 
-#### [DeepEdit Inference](./inference.ipynb)
+#### [DeepEdit Inference](./infoANDinference.ipynb)
 
 This notebook helps to run any pre-transforms before running inference over a DeepEdit single label model.
 It also helps to run post-transforms to get the final label mask.
diff --git a/generation/README.md b/generation/README.md
index 6c4fe96aa..34b492c4a 100644
--- a/generation/README.md
+++ b/generation/README.md
@@ -74,10 +74,10 @@ Example shows the use cases of applying a spatial VAE to a 2D synthesis example.
 Example shows the use cases of applying a spatial VAE to a 3D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones.
 
 ## Performing anomaly detection with diffusion models: [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb)
-Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance).
+Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb).
 
 ## 2D super-resolution using diffusion models: [using torch](./2d_super_resolution/2d_sd_super_resolution.ipynb) and [using torch lightning](./2d_super_resolution/2d_sd_super_resolution_lightning.ipynb).
 Examples show how to perform super-resolution in 2D, using PyTorch and PyTorch Lightning.
 
-## [Guiding the synthetic process using a semantic encoder](./2d_diffusion_autoencoder/2d_diffusion_autoencoder.ipynb)
+## [Guiding the synthetic process using a semantic encoder](./2d_diffusion_autoencoder/2d_diffusion_autoencoder_tutorial.ipynb)
 Example shows how to train a DDPM and an encoder simultaneously, resulting in the latents of the encoder guiding the inference process of the DDPM.
diff --git a/monailabel/README.md b/monailabel/README.md
index 3a06ba2a5..ecd33d6f8 100644
--- a/monailabel/README.md
+++ b/monailabel/README.md
@@ -24,7 +24,7 @@ Each tutorial provides MONAI Label plugin installation steps associated with the
     - [MONAILabel: Pancreas Tumor Segmentation with 3D Slicer](monailabel_bring_your_own_data.ipynb): Pancreas and tumor segmentation with CT scans in 3D Slicer.
     - [MONAILabel: Multi-organ Segmentation with 3D Slicer](monailabel_monaibundle_3dslicer_multiorgan_seg.ipynb): Multi-organ segmentation with CT scans in 3D Slicer.
     - [MONAILabel: Whole Body CT Segmentation with 3D Slicer](monailabel_wholebody_totalSegmentator_3dslicer.ipynb): Whole body (104 structures) segmentation with CT scans.
-    - [MONAILabel: Lung nodule CT Detection with 3D Slicer](monaibundle_3dslicer_lung_nodule_detection.ipynb): Lung nodule detection task with CT scans.
+    - [MONAILabel: Lung nodule CT Detection with 3D Slicer](monailabel_monaibundle_3dslicer_lung_nodule_detection.ipynb): Lung nodule detection task with CT scans.
 - **Pathology App**:
   - Viewer: [QuPath](https://qupath.github.io/) | Datastore: Local | Task: Segmentation
     - [MONAILabel: Nuclei Segmentation with QuPath](monailabel_pathology_nuclei_segmentation_QuPath.ipynb) Nuclei segmentation with QuPath setup and Nuclick models.