diff --git a/docs/images/SSL_Overview_Figure.png b/docs/images/SSL_Overview_Figure.png
new file mode 100644
index 0000000000..68fa1af576
Binary files /dev/null and b/docs/images/SSL_Overview_Figure.png differ
diff --git a/docs/images/dints-overview.png b/docs/images/dints-overview.png
new file mode 100644
index 0000000000..e5f592a8e5
Binary files /dev/null and b/docs/images/dints-overview.png differ
diff --git a/docs/images/mil-patches.jpg b/docs/images/mil-patches.jpg
new file mode 100644
index 0000000000..fd904943be
Binary files /dev/null and b/docs/images/mil-patches.jpg differ
diff --git a/docs/source/whatsnew.rst b/docs/source/whatsnew.rst
index e1f118cdf6..1f27d651db 100644
--- a/docs/source/whatsnew.rst
+++ b/docs/source/whatsnew.rst
@@ -6,6 +6,7 @@ What's New
 .. toctree::
    :maxdepth: 1
 
+   whatsnew_0_8.md
    whatsnew_0_7.md
    whatsnew_0_6.md
    whatsnew_0_5.md
diff --git a/docs/source/whatsnew_0_7.md b/docs/source/whatsnew_0_7.md
index 748729e94d..6df64948b0 100644
--- a/docs/source/whatsnew_0_7.md
+++ b/docs/source/whatsnew_0_7.md
@@ -1,4 +1,4 @@
-# What's new in 0.7 🎉🎉
+# What's new in 0.7
 
 - Performance enhancements with profiling and tuning guides
 - Major usability improvements in `monai.transforms`
diff --git a/docs/source/whatsnew_0_8.md b/docs/source/whatsnew_0_8.md
new file mode 100644
index 0000000000..cb56510ede
--- /dev/null
+++ b/docs/source/whatsnew_0_8.md
@@ -0,0 +1,56 @@
+# What's new in 0.8 🎉🎉
+
+- Differentiable neural network topology search
+- Multiple instance learning for digital pathology WSI analysis
+- Self-supervised representation learning
+- Major usability improvements in `monai.transforms`
+
+## Differentiable neural network topology search
+MONAI integrates `DiNTS`: [Differentiable Neural Network Topology Search for 3D
+Medical Image Segmentation](https://arxiv.org/abs/2103.15954). The neural
+architecture search module supports flexible multi-path topology search with
+high search efficiency and budgeted memory usage.
+
+It provides a topology guaranteed discretization algorithm and a
+discretization-aware topology loss for the search stage to minimize the
+discretization gap. The module is memory usage aware and is able to search 3D
+networks with different GPU memory requirements. For more details, please checkout the
+[DiNTS tutorial](https://github.com/Project-MONAI/tutorials/tree/master/automl).
+
+![DiNTS](../images/dints-overview.png)
+
+## Multiple instance learning for digital pathology WSI analysis
+For [classification of digital pathology whole slide images
+(WSI)](https://arxiv.org/abs/2111.01556), MONAI introduces new transforms and
+network modules for multiple instance learning. These include self-attention
+transformer blocks for explicitly accounting of the dependencies between instances
+(image patches) during training. For more details,
+please checkout the [multi-instance tutorial](https://github.com/Project-MONAI/tutorials/tree/master/pathology/multiple_instance_learning)
+
+![multi-instance](../images/mil-patches.jpg)
+
+## Self-supervised representation learning
+MONAI starts to explore self-supervised representation learning in this
+milestone release. The Vision Transformer has been extended to learn from self-supervised
+reconstruction tasks with various data augmentation and a regularized
+contrastive loss. The weights of the pre-trained backbone could be used to
+enhance the performance of the novel downstream deep learning tasks.
+
+The [tutorial](https://github.com/Project-MONAI/tutorials/tree/master/self_supervised_pretraining)
+shows how to generate a good set of pre-trained weights using unlabeled data
+with self-supervised tasks, then use the pre-trained weights to perform
+fine-tuning on a fully supervised volumetric segmentation task using a transformer based `UNETR`.
+
+![self-supervised](../images/SSL_Overview_Figure.png)
+
+## Major usability improvements in `monai.transforms`
+`monai.transforms` are now more flexible and easy to use in version 0.8.
+- Input type handling and backend APIs are improved to support both
+  NumPy and PyTorch where possible.
+- Visual examples are added to the documentation to illustrate the effects of
+  various image processing.
+- New visualization utilities are provided and enhanced for quick qualitative
+  assessments of the model by visualizing, for example, the volumetric image
+  inputs, segmentation maps, and intermediate feature maps.
+  The visualization tutorial is available for
+  [TensorBoard utility, `matshow3d` and `blend_images`](https://github.com/Project-MONAI/tutorials/blob/master/modules/transform_visualization.ipynb).