1015 Move examples to tutorials repo (#1051)

* [DLMED] move examples to tutorials repo

Signed-off-by: Nic Ma <nma@nvidia.com>

* [DLMED] update docs

Signed-off-by: Nic Ma <nma@nvidia.com>

Author: Nic-Ma

README.md

@@ -51,7 +51,7 @@ For more details, please refer to [the installation guide](https://docs.monai.io
 
 [MedNIST demo](https://colab.research.google.com/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe) and [MONAI for PyTorch Users](https://colab.research.google.com/drive/1boqy7ENpKrqaJoxFlbHIBnIODAs1Ih1T) are available on Colab.
 
-Examples are located at [monai/examples](https://github.com/Project-MONAI/MONAI/tree/master/examples), notebook tutorials are located at [Project-MONAI/Tutorials](https://github.com/Project-MONAI/Tutorials).
+Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials).
 
 Technical documentation is available at [docs.monai.io](https://docs.monai.io).
 

docs/source/highlights.md

@@ -47,7 +47,7 @@ transformations. These currently include, for example:
 - `Rand2DElastic`: Random elastic deformation and affine in 2D
 - `Rand3DElastic`: Random elastic deformation and affine in 3D
 
-[2D transforms tutorial](https://github.com/Project-MONAI/Tutorials/blob/master/transforms_demo_2d.ipynb) shows the detailed usage of several MONAI medical image specific transforms.
+[2D transforms tutorial](https://github.com/Project-MONAI/tutorials/blob/master/modules/transforms_demo_2d.ipynb) shows the detailed usage of several MONAI medical image specific transforms.
 ![image](../images/medical_transforms.png)
 
 ### 3. Fused spatial transforms and GPU acceleration
@@ -66,13 +66,13 @@ affine = Affine(
 # convert the image using bilinear interpolation
 new_img = affine(image, spatial_size=(300, 400), mode='bilinear')
 ```
-Experiments and test results are available at [Fused transforms test](https://github.com/Project-MONAI/Tutorials/blob/master/transform_speed.ipynb).
+Experiments and test results are available at [Fused transforms test](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/transform_speed.ipynb).
 
-Currently all the geometric image transforms (Spacing, Zoom, Rotate, Resize, etc.) are designed based on the PyTorch native interfaces. [Geometric transforms tutorial](https://github.com/Project-MONAI/Tutorials/blob/master/3d_image_transforms.ipynb) indicates the usage of affine transforms with 3D medical images.
+Currently all the geometric image transforms (Spacing, Zoom, Rotate, Resize, etc.) are designed based on the PyTorch native interfaces. [Geometric transforms tutorial](https://github.com/Project-MONAI/tutorials/blob/master/modules/3d_image_transforms.ipynb) indicates the usage of affine transforms with 3D medical images.
 ![image](../images/affine.png)
 
 ### 4. Randomly crop out batch images based on positive/negative ratio
-Medical image data volume may be too large to fit into GPU memory. A widely-used approach is to randomly draw small size data samples during training and run a “sliding window” routine for inference.  MONAI currently provides general random sampling strategies including class-balanced fixed ratio sampling which may help stabilize the patch-based training process. A typical example is in [Spleen 3D segmentation tutorial](https://github.com/Project-MONAI/Tutorials/blob/master/spleen_segmentation_3d.ipynb), which achieves the class-balanced sampling with `RandCropByPosNegLabel` transform.
+Medical image data volume may be too large to fit into GPU memory. A widely-used approach is to randomly draw small size data samples during training and run a “sliding window” routine for inference.  MONAI currently provides general random sampling strategies including class-balanced fixed ratio sampling which may help stabilize the patch-based training process. A typical example is in [Spleen 3D segmentation tutorial](https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/spleen_segmentation_3d.ipynb), which achieves the class-balanced sampling with `RandCropByPosNegLabel` transform.
 
 ### 5. Deterministic training for reproducibility
 Deterministic training support is necessary and important for deep learning research, especially in the medical field. Users can easily set the random seed to all the random transforms in MONAI locally and will not affect other non-deterministic modules in the user's program.
@@ -110,23 +110,23 @@ MONAI also provides post-processing transforms for handling the model outputs. C
 - Removing segmentation noise based on Connected Component Analysis, as below figure (c).
 - Extracting contour of segmentation result, which can be used to map to original image and evaluate the model, as below figure (d) and (e).
 
-After applying the post-processing transforms, it's easier to compute metrics, save model output into files or visualize data in the TensorBoard. [Post transforms tutorial](https://github.com/Project-MONAI/Tutorials/blob/master/post_transforms.ipynb) shows an example with several main post transforms.
+After applying the post-processing transforms, it's easier to compute metrics, save model output into files or visualize data in the TensorBoard. [Post transforms tutorial](https://github.com/Project-MONAI/tutorials/blob/master/modules/post_transforms.ipynb) shows an example with several main post transforms.
 ![image](../images/post_transforms.png)
 
 ### 9. Integrate third-party transforms
 The design of MONAI transforms emphasis code readability and usability. It works for array data or dictionary-based data. MONAI also provides `Adaptor` tools to accommodate different data format for 3rd party transforms. To convert the data shapes or types, utility transforms such as `ToTensor`, `ToNumpy`, `SqueezeDim` are also provided. So it's easy to enhance the transform chain by seamlessly integrating transforms from external packages, including: `ITK`, `BatchGenerator`, `TorchIO` and `Rising`.
 
-For more details, please check out the tutorial: [integrate 3rd party transforms into MONAI program](https://github.com/Project-MONAI/Tutorials/blob/master/integrate_3rd_party_transforms.ipynb).
+For more details, please check out the tutorial: [integrate 3rd party transforms into MONAI program](https://github.com/Project-MONAI/tutorials/blob/master/modules/integrate_3rd_party_transforms.ipynb).
 
 ## Datasets
 ### 1. Cache IO and transforms data to accelerate training
 Users often need to train the model with many (potentially thousands of) epochs over the data to achieve the desired model quality. A native PyTorch implementation may repeatedly load data and run the same preprocessing steps for every epoch during training, which can be time-consuming and unnecessary, especially when the medical image volumes are large.
 
-MONAI provides a multi-threads `CacheDataset` to accelerate these transformation steps during training by storing the intermediate outcomes before the first randomized transform in the transform chain. Enabling this feature could potentially give 10x training speedups in the [Datasets experiment](https://github.com/Project-MONAI/Tutorials/blob/master/dataset_type_performance.ipynb).
+MONAI provides a multi-threads `CacheDataset` to accelerate these transformation steps during training by storing the intermediate outcomes before the first randomized transform in the transform chain. Enabling this feature could potentially give 10x training speedups in the [Datasets experiment](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/dataset_type_performance.ipynb).
 ![image](../images/cache_dataset.png)
 
 ### 2. Cache intermediate outcomes into persistent storage
-The `PersistentDataset` is similar to the CacheDataset, where the intermediate cache values are persisted to disk storage for rapid retrieval between experimental runs (as is the case when tuning hyperparameters), or when the entire data set size exceeds available memory. The `PersistentDataset` could achieve similar performance when comparing to `CacheDataset` in [Datasets experiment](https://github.com/Project-MONAI/Tutorials/blob/master/dataset_type_performance.ipynb).
+The `PersistentDataset` is similar to the CacheDataset, where the intermediate cache values are persisted to disk storage for rapid retrieval between experimental runs (as is the case when tuning hyperparameters), or when the entire data set size exceeds available memory. The `PersistentDataset` could achieve similar performance when comparing to `CacheDataset` in [Datasets experiment](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/dataset_type_performance.ipynb).
 ![image](../images/datasets_speed.png)
 
 ### 3. Zip multiple PyTorch datasets and fuse the output
@@ -148,7 +148,7 @@ dataset = ZipDataset([DatasetA(), DatasetB()], transform)
 ### 4. Predefined Datasets for public medical data
 To quickly get started with popular training data in the medical domain, MONAI provides several data-specific Datasets(like: `MedNISTDataset`, `DecathlonDataset`, etc.), which include downloading, extracting data files and support generation of training/evaluation items with transforms. And they are flexible that users can easily modify the JSON config file to change the default behaviors.
 
-MONAI always welcome new contributions of public datasets, please refer to existing Datasets and leverage the download and extracting APIs, etc. [Public datasets tutorial](https://github.com/Project-MONAI/Tutorials/blob/master/public_datasets.ipynb) indicates how to quickly set up training workflows with `MedNISTDataset` and `DecathlonDataset` and how to create a new `Dataset` for public data.
+MONAI always welcome new contributions of public datasets, please refer to existing Datasets and leverage the download and extracting APIs, etc. [Public datasets tutorial](https://github.com/Project-MONAI/tutorials/blob/master/modules/public_datasets.ipynb) indicates how to quickly set up training workflows with `MedNISTDataset` and `DecathlonDataset` and how to create a new `Dataset` for public data.
 
 The common workflow of predefined datasets:
 ![image](../images/dataset_progress.png)
@@ -187,7 +187,7 @@ A typical process is:
 4. Save the results to file or compute some evaluation metrics.
 ![image](../images/sliding_window.png)
 
-The [Spleen 3D segmentation tutorial](https://github.com/Project-MONAI/Tutorials/blob/master/spleen_segmentation_3d.ipynb) leverages `SlidingWindow` inference for validation.
+The [Spleen 3D segmentation tutorial](https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/spleen_segmentation_3d.ipynb) leverages `SlidingWindow` inference for validation.
 
 ### 2. Metrics for medical tasks
 Various useful evaluation metrics have been used to measure the quality of medical image specific models. MONAI already implemented mean Dice score for segmentation tasks and the area under the ROC curve for classification tasks. We continue to integrate more options.