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multimodal/openi_multilabel_classification_transchex/README.md

@@ -1,24 +1,23 @@
 # Preprocessing Open-I Dataset
 
-The Open-I dataset provides a collection of 3,996 radiology reports 
-with 8,121 associated images in PA, AP and lateral views. In this tutorial, we utilize the images from fronal view with their corresponding reports for training and 
-evaluation of the TransChex model. The chest x-ray images and reports are originally from the Indiana University hospital (see the licencing information below). 
-The 14 finding categories in this work include Atelectasis, Cardiomegaly, Consolidation, Edema, Enlarged-Cardiomediastinum, Fracture, Lung-Lesion, Lung-Opacity, No-Finding, Pleural-Effusion, Pleural-Other, Pneumonia, Pneumothorax and Support-Devices. More information can be found in the following link:      
+The Open-I dataset provides a collection of 3,996 radiology reports
+with 8,121 associated images in PA, AP and lateral views. In this tutorial, we utilize the images from fronal view with their corresponding reports for training and
+evaluation of the TransChex model. The chest x-ray images and reports are originally from the Indiana University hospital (see the licencing information below).
+The 14 finding categories in this work include Atelectasis, Cardiomegaly, Consolidation, Edema, Enlarged-Cardiomediastinum, Fracture, Lung-Lesion, Lung-Opacity, No-Finding, Pleural-Effusion, Pleural-Other, Pneumonia, Pneumothorax and Support-Devices. More information can be found in the following link:
 https://openi.nlm.nih.gov/faq
 
 License: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
 
 In this section, we provide the steps that are needed for preprocessing the Open-I dataset for
 the multi-label disease classification tutorial using TransCheX model. As a result, once the following steps are
-completed, the dataset can be readily used for the tutorial. 
+completed, the dataset can be readily used for the tutorial.
 
 ### Preprocessing Steps
-1) Create a new folder named 'monai_data' for downloading the raw data and preprocessing. 
+1) Create a new folder named 'monai_data' for downloading the raw data and preprocessing.
 2) Download the chest X-ray images in PNG format from this [link](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_png.tgz). Copy the downloaded file (NLMCXR_png.tgz)
-to 'monai_data' directory and extract it. 
+to 'monai_data' directory and extract it.
 3) Download the reports in XML format from this [link](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_reports.tgz). Copy the downloaded file (NLMCXR_reports.tgz)
 to 'monai_data' directory and extract it.
 4) Download the splits of train, validation and test datasets from this [link](https://drive.google.com/u/1/uc?id=1_CThgwbDQPeTrr2Gvi6zflqr32_5t87j&export=download). Copy the downloaded file (TransChex_openi.zip)
 to 'monai_data' directory and extract it.
-5) Run 'preprocess_openi.py' to process the images and reports.  
-
+5) Run 'preprocess_openi.py' to process the images and reports.

multimodal/openi_multilabel_classification_transchex/preprocess_openi.py

@@ -117,4 +117,3 @@ def create_report(img_names_list_, report_list_, gt_list_, save_add):
     create_report(dataset["img_name"], dataset["report"], dataset["gt"], dataset["save_add"])
 
 print('Processed Dataset Files Are Saved !')
-