Update README.md

originally from https://github.com/Project-MONAI/MONAI/blob/8310bb72f838d6a8f42b7f4e2141cf75bb218519/examples/README.md

Author: wyli

README.md

@@ -1,2 +1,79 @@
 # MONAI Tutorials
 This repository hosts the MONAI tutorials.
+
+### 1. Requirements
+Most of the examples and tutorials require
+[matplotlib](https://matplotlib.org/) and [Jupyter Notebook](https://jupyter.org/).
+
+These could be installed by:
+```bash
+python -m pip install -U pip
+python -m pip install -U matplotlib
+python -m pip install -U notebook
+```
+
+Some of the examples may require optional dependencies. In case of any optional import errors,
+please install the relevant packages according to the error message.
+Or install all optional requirements by:
+```
+pip install -r https://raw.githubusercontent.com/Project-MONAI/MONAI/master/requirements-dev.txt
+```
+### 2. List of notebooks
+#### [3d_image_transforms](./3d_image_transforms.ipynb)
+This notebook demonstrates the transformations on volumetric images.
+#### [automatic_mixed_precision](./automatic_mixed_precision.ipynb)
+This tutorial shows how to apply the automatic mixed precision(AMP) feature of PyTorch into training and evaluation programs.
+And compares the training speed and memory usage with/without AMP.
+#### [brats_segmentation_3d](./brats_segmentation_3d.ipynb)
+This tutorial shows how to construct a training workflow of multi-labels segmentation task based on [MSD Brain Tumor dataset](http://medicaldecathlon.com).
+#### [cache_dataset_speed](./cache_dataset_speed.ipynb)
+This tutorial shows how to accelerate PyTorch medical DL program based on MONAI `CacheDataset`.
+#### [integrate_3rd_party_transforms](./integrate_3rd_party_transforms.ipynb)
+This tutorial shows how to integrate 3rd party transforms into MONAI program.
+Mainly shows transforms from BatchGenerator, TorchIO, Rising and ITK.
+#### [mednist_GAN_tutorial](./mednist_GAN_tutorial.ipynb)
+This notebook illustrates the use of MONAI for training a network to generate images from a random input tensor.
+A simple GAN is employed to do with a separate Generator and Discriminator networks.
+#### [mednist_GAN_workflow](./mednist_GAN_workflow.ipynb)
+This notebook shows the `GanTrainer`, a MONAI workflow engine for modularized adversarial learning. Train a medical image reconstruction network using the MedNIST hand CT scan dataset. Based on the tutorial.
+#### [mednist_tutorial](./mednist_tutorial.ipynb)
+This notebook shows how to easily integrate MONAI features into existing PyTorch programs.
+It's based on the MedNIST dataset which is very suitable for beginners as a tutorial.
+The content is also available as [a Colab tutorial](https://colab.research.google.com/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe).
+#### [models_ensemble](./models_ensemble.ipynb)
+This tutorial shows how to leverage `EnsembleEvaluator`, `MeanEnsemble` and `VoteEnsemble` modules in MONAI to set up ensemble program.
+#### [multi_gpu_test](./multi_gpu_test.ipynb)
+This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
+#### [nifti_read_example](./nifti_read_example.ipynb)
+Illustrate reading NIfTI files and iterating over image patches of the volumes loaded from them.
+#### [persistent_dataset_speed](./persistent_dataset_speed.ipynb)
+This notebook shows `PersistentDataset` that processes original data sources through the non-random transforms on first use.
+And stores these intermediate tensor values to an on-disk persistence representation.
+The intermediate processed tensors are loaded from disk on each use for processing by the random-transforms for each analysis request.
+#### [post_transforms](./post_transforms.ipynb)
+This notebook shows the usage of several post transforms based on the model output of spleen segmentation task.
+#### [public_datasets](./public_datasets.ipynb)
+This notebook shows how to quickly set up training workflow based on `MedNISTDataset` and `DecathlonDataset`, and how to create a new dataset.
+#### [spleen_segmentation_3d](./spleen_segmentation_3d.ipynb)
+This notebook is an end-to-end training and evaluation example of 3D segmentation based on [MSD Spleen dataset](http://medicaldecathlon.com).
+The example shows the flexibility of MONAI modules in a PyTorch-based program:
+- Transforms for dictionary-based training data structure.
+- Load NIfTI images with metadata.
+- Scale medical image intensity with expected range.
+- Crop out a batch of balanced image patch samples based on positive / negative label ratio.
+- Cache IO and transforms to accelerate training and validation.
+- 3D UNet, Dice loss function, Mean Dice metric for 3D segmentation task.
+- Sliding window inference.
+- Deterministic training for reproducibility.
+#### [spleen_segmentation_3d_lightning](./spleen_segmentation_3d_lightning.ipynb)
+This notebook shows how MONAI may be used in conjunction with the [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) framework.
+#### [unet_segmentation_3d_catalyst](./unet_segmentation_3d_catalyst.ipynb)
+This notebook shows how MONAI may be used in conjunction with the [Catalyst](https://github.com/catalyst-team/catalyst) framework.
+#### [transform_speed](./transform_speed.ipynb)
+Illustrate reading NIfTI files and test speed of different transforms on different devices.
+#### [transforms_demo_2d](./transforms_demo_2d.ipynb)
+This notebook demonstrates the image transformations on histology images using
+[the GlaS Contest dataset](https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/download/).
+#### [unet_segmentation_3d_ignite](./unet_segmentation_3d_ignite.ipynb)
+This notebook is an end-to-end training & evaluation example of 3D segmentation based on synthetic dataset.
+The example is a PyTorch Ignite program and shows several key features of MONAI, especially with medical domain specific transforms and event handlers.