From fbdeb1911f2b4567ccae516e9c13dc1b9e68888e Mon Sep 17 00:00:00 2001 From: Zhaojing Luo Date: Wed, 4 Jun 2025 18:58:08 +0800 Subject: [PATCH 1/3] Update readme.md for the Hetomalogical disease application Update readme.md for the Hetomalogical disease application --- examples/healthcare/application/Hematologic_Disease/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 From 152d965b118b57c40e0b11fc311880c63ca29e08 Mon Sep 17 00:00:00 2001 From: Zhaojing Luo Date: Wed, 4 Jun 2025 19:00:27 +0800 Subject: [PATCH 2/3] Update README.md for the kidney disease application Update README.md for the kidney disease application --- examples/healthcare/application/Kidney_Disease/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) 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 ``` From 7f4254271d4b6108214d7848c7f76fe19621b391 Mon Sep 17 00:00:00 2001 From: Zhaojing Luo Date: Wed, 4 Jun 2025 19:01:36 +0800 Subject: [PATCH 3/3] Update README.md for the Thyroid Eye Disease application Update README.md for the Thyroid Eye Disease application --- examples/healthcare/application/Thyroid_Eye_Disease/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) 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)