diff --git a/examples/healthcare/application/Hematologic_Disease/readme.md b/examples/healthcare/application/Hematologic_Disease/readme.md
index 26564c0d2..db5a21146 100644
--- a/examples/healthcare/application/Hematologic_Disease/readme.md
+++ b/examples/healthcare/application/Hematologic_Disease/readme.md
@@ -41,7 +41,7 @@ The source images with resolution 3×360×363 pixels are center-cropped into 3×
 
 ## Running instructions
 
-1. Download the pre-processed [BloodMnist dataset](https://github.com/lzjpaul/singa-healthcare/blob/main/data/bloodmnist/bloodmnist.tar.gz) to a folder(pathToDataset), which contains a few training samples and test samples. For the complete BloodMnist dataset, please download it via this [link](https://github.com/gzrp/bloodmnist/blob/master/bloodmnist.zip).
+1. Download the pre-processed [BloodMnist dataset](https://github.com/lzjpaul/singa-healthcare/blob/main/data/bloodmnist/bloodmnist.tar.gz) to the folder (pathToDataset), which contains a few training samples and test samples. For the complete BloodMnist dataset, please download it via this [link](https://github.com/gzrp/bloodmnist/blob/master/bloodmnist.zip).
 
 2. Start the training
 
diff --git a/examples/healthcare/application/Kidney_Disease/README.md b/examples/healthcare/application/Kidney_Disease/README.md
index 0a3979e79..a6bfaefc5 100644
--- a/examples/healthcare/application/Kidney_Disease/README.md
+++ b/examples/healthcare/application/Kidney_Disease/README.md
@@ -38,9 +38,9 @@ The dataset used in this task is MIMIC-III after preprocessed. The features are
 ## Instruction
 Before starting to use this model for kidney disease prediction, download the sample dataset for kidney disease prediction: https://github.com/lzjpaul/singa-healthcare/tree/main/data/kidney
 
-The provided dataset is from MIMIC-III, which has been pre-processed. And the dataset contains 100 samples for model testing.
+The provided dataset is from MIMIC-III, which has been pre-processed. The dataset contains 100 samples for model testing.
 
-Please download the dataset to a folder(pathToDataset), and then pass the path to run the codes using the following command:
+Please download the dataset to the folder (pathToDataset), and then pass the path to run the codes using the following command:
 ```bash
 python train.py kidneynet -dir pathToDataset
 ```
diff --git a/examples/healthcare/application/Thyroid_Eye_Disease/README.md b/examples/healthcare/application/Thyroid_Eye_Disease/README.md
index 755ef96d1..5e19dd666 100644
--- a/examples/healthcare/application/Thyroid_Eye_Disease/README.md
+++ b/examples/healthcare/application/Thyroid_Eye_Disease/README.md
@@ -24,7 +24,7 @@ We have successfully applied the idea of prototype loss in various medical image
 
 ## Running instructions
 
-1. Download the [CIFAR-10 python version](https://www.cs.toronto.edu/~kriz/cifar.html) to a folder(pathToDataset).
+1. Download the [CIFAR-10 python version](https://www.cs.toronto.edu/~kriz/cifar.html) to the folder (pathToDataset).
 
 2. Start the training
 
@@ -34,4 +34,4 @@ python train.py tedctnet -dir pathToDataset
 
 ## reference
 
-[Robust Classification with Convolutional Prototype Learning](https://arxiv.org/abs/1805.03438)
\ No newline at end of file
+[Robust Classification with Convolutional Prototype Learning](https://arxiv.org/abs/1805.03438)