[DLMED] update highlights
@@ -121,11 +121,11 @@ For more details, please check out the tutorial: [integrate 3rd party transforms
### 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 [CacheDataset experiment](https://github.com/Project-MONAI/Tutorials/blob/master/cache_dataset_speed.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/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 [PersistentDataset experiment](https://github.com/Project-MONAI/Tutorials/blob/master/persistent_dataset_speed.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/dataset_type_performance.ipynb).
![image](../images/datasets_speed.png)
### 3. Zip multiple PyTorch datasets and fuse the output
