212 update README of examples (Project-MONAI#214)

* [DLMED] update README of examples

* [DLMED] add dataset links to README

* revise readme

* remove duplicated yml

Co-authored-by: Wenqi Li <[email protected]>

examples/README.md

@@ -1,3 +1,4 @@
+### 1. Requirements
 Most of the examples and tutorials require
 [matplotlib](https://matplotlib.org/) and [Jupyter Notebook](https://jupyter.org/).
 
@@ -7,3 +8,39 @@ python -m pip install -U pip
 python -m pip install -U matplotlib
 python -m pip install -U notebook
 ```
+
+### 2. List of examples
+#### 1. [classification_3d](https://github.com/Project-MONAI/MONAI/tree/master/examples/classification_3d)
+Training and evaluation examples of 3D classification based on DenseNet3D and [IXI dataset](https://brain-development.org/ixi-dataset):
+The examples are standard PyTorch programs and have both dictionary-based and array-based transformation versions.
+#### 2. [classification_3d_ignite](https://github.com/Project-MONAI/MONAI/tree/master/examples/classification_3d_ignite)
+Training and evaluation examples of 3D classification based on DenseNet3D and [IXI dataset](https://brain-development.org/ixi-dataset):
+The examples are PyTorch ignite programs and have both dictionary-based and array-based transformation versions.
+#### 3. [notebooks/multi_gpu_test](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/multi_gpu_test.ipynb)
+This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
+#### 4. [notebooks/nifti_read_example](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/nifti_read_example.ipynb)
+Illustrate reading NIfTI files and iterating over image patches of the volumes loaded from them.
+#### 5. [notebooks/spleen_segmentation_3d](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/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.
+- 3D UNet, Dice loss function, Mean Dice metric for 3D segmentation task.
+- Sliding window inference.
+- Deterministic training for reproducibility.
+#### 6. [notebooks/transform_speed](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/transform_speed.ipynb)
+Illustrate reading NIfTI files and test speed of different transforms on different devices.
+#### 7. [notebooks/transforms_demo_2d](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/transforms_demo_2d.ipynb)
+This notebook demonstrates the medical domain specific transforms on 2D medical images.
+#### 8. [notebooks/unet_segmentation_3d_ignite](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/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.
+#### 9. [segmentation_3d](https://github.com/Project-MONAI/MONAI/tree/master/examples/segmentation_3d)
+Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset.
+The examples are standard PyTorch programs and have both dictionary-based and array-based versions.
+#### 10. [segmentation_3d_ignite](https://github.com/Project-MONAI/MONAI/tree/master/examples/segmentation_3d_ignite)
+Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset.
+The examples are PyTorch Ignite programs and have both dictionary-base and array-based transformations.