Fix broken relative links in README files (#2083)

Fixes # .

### Description
Fixes five relative links in README files that point to paths that do
not exist on `main`:
- `README.md`: the vista3d entry pointed to `./3d_segmentation/vista3d`;
the tutorial moved to `./vista_3d` in #1835.
- `generation/README.md`: the classifier guidance link was missing the
`anomaly_detection/` folder (the heading line above already uses the
right path), and the diffusion autoencoder link used
`2d_diffusion_autoencoder.ipynb` instead of
`2d_diffusion_autoencoder_tutorial.ipynb`.
- `monailabel/README.md`: the lung nodule detection link was missing the
`monailabel_` prefix of
`monailabel_monaibundle_3dslicer_lung_nodule_detection.ipynb`.
- `deepedit/ignite/README.md`: the DeepEdit Inference link pointed to
`./inference.ipynb`; the notebook in that folder is
`infoANDinference.ipynb`.

Docs-only change, no notebooks touched.

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Avoid including large-size files in the PR.
- [x] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [x] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **Documentation**
* Updated README links for the Vista3D notebook and DeepEdit inference
guide.
* Corrected links to the anomaly-detection classifier-free-guidance and
semantic-encoder tutorials.
* Updated the MONAIBUNDLE lung nodule detection tutorial link. These
changes help readers navigate to the referenced notebook and tutorial
pages.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Signed-off-by: Pratik Gandhi <travpreneur@gmail.com>

Author: pratikgx

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>

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.

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.

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.