[DLMED] update example README
@@ -11,10 +11,10 @@ python -m pip install -U notebook
### 2. List of examples
#### [classification_3d](./classification_3d)
-Training and evaluation examples of 3D classification based on DenseNet3D and [IXI dataset](https://brain-development.org/ixi-dataset):
+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.
#### [classification_3d_ignite](./classification_3d_ignite)
-Training and evaluation examples of 3D classification based on DenseNet3D and [IXI dataset](https://brain-development.org/ixi-dataset):
+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.
#### [notebooks/3d_image_transforms](./notebooks/3d_image_transforms.ipynb)
This notebook demonstrates the transformations on volumetric images.
@@ -36,7 +36,7 @@ This notebook shows `PersistentDataset` that processes original data sources thr
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.
#### [notebooks/spleen_segmentation_3d](./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):
+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.
@@ -55,11 +55,13 @@ 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/).
#### [notebooks/unet_segmentation_3d_ignite](./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.
+The example is a PyTorch Ignite program and shows several key features of MONAI, especially with medical domain specific transforms and event handlers.
#### [segmentation_3d](./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.
#### [segmentation_3d_ignite](./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.
+#### [workflows](./workflows)
+Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset.
+The examples are built with MONAI workflows, mainly contain: trainer/evaluator, handlers, post_transforms, etc.
