Add TransCheX Tutorial

multimodal/openi_multilabel_classification_transchex/README.md

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+# 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:
+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.
+
+### Preprocessing Steps
+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.
+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=1jvT0jVl9mgtWy4cS7LYbF43bQE4mrXAY&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.

multimodal/openi_multilabel_classification_transchex/preprocess_openi.py

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+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from os import listdir
+from os.path import isfile, join
+import numpy as np
+from xml.dom import minidom
+from PIL import Image
+import pandas as pd
+import xml.etree.ElementTree as ET
+
+def create_report(img_names_list_, report_list_, gt_list_, save_add):
+    pd.DataFrame({'id': img_names_list_, 'report': report_list_, 'Atelectasis': gt_list_[:, 0],
+                  'Cardiomegaly': gt_list_[:, 1], 'Consolidation': gt_list_[:, 2],'Edema': gt_list_[:, 3],
+                  'Enlarged-Cardiomediastinum': gt_list_[:, 4], 'Fracture': gt_list_[:, 5], 'Lung-Lesion': gt_list_[:, 6],
+                  'Lung-Opacity': gt_list_[:, 7], 'No-Finding': gt_list_[:, 8], 'Pleural-Effusion': gt_list_[:, 9],
+                  'Pleural_Other': gt_list_[:, 10], 'Pneumonia': gt_list_[:, 11], 'Pneumothorax': gt_list_[:, 12],
+                  'Support-Devices': gt_list_[:, 13]}).to_csv(save_add, index=False)
+
+report_file_add= './monai_data/dataset_orig/NLMCXR_reports/ecgen-radiology'
+img_file_add= './monai_data/dataset_orig/NLMCXR_png'
+img_save_add = './monai_data/dataset_proc/images'
+report_train_save_add = './monai_data/dataset_proc/train.csv'
+report_val_save_add = './monai_data/dataset_proc/validation.csv'
+report_test_save_add = './monai_data/dataset_proc/test.csv'
+
+if not os.path.isdir(img_save_add):
+    os.makedirs(img_save_add)
+report_files = [f for f in listdir(report_file_add) if isfile(join(report_file_add, f))]
+
+train_data = np.load('./train.npy', allow_pickle=True).item()
+train_data_id = train_data['id_GT']
+train_data_gt = train_data['label_GT']
+
+val_data = np.load('./validation.npy', allow_pickle=True).item()
+val_data_id = val_data['id_GT']
+val_data_gt = val_data['label_GT']
+
+test_data = np.load('./test.npy', allow_pickle=True).item()
+test_data_id = test_data['id_GT']
+test_data_gt = test_data['label_GT']
+
+all_cases = np.union1d(np.union1d(train_data_id, val_data_id), test_data_id)
+
+img_names_list_train = []
+img_names_list_val = []
+img_names_list_test = []
+
+report_list_train = []
+report_list_val = []
+report_list_test = []
+
+gt_list_train = []
+gt_list_val = []
+gt_list_test = []
+
+for file in report_files:
+    print('Processing {}'.format(file))
+    add_xml = os.path.join(report_file_add, file)
+    docs = minidom.parse(add_xml)
+    tree = ET.parse(add_xml)
+    for node in tree.iter('AbstractText'):
+        i = 0
+        for elem in node.iter():
+            if elem.attrib['Label'] == "FINDINGS":
+                if elem.text == None:
+                    report = "FINDINGS : "
+                else:
+                    report = "FINDINGS : " + elem.text
+            elif elem.attrib['Label'] == "IMPRESSION":
+                if elem.text == None:
+                    report = report + " IMPRESSION : "
+                else:
+                    report = report + " IMPRESSION : " + elem.text
+    images = docs.getElementsByTagName("parentImage")
+    for i in images:
+        img_name = i.getAttribute("id") + '.png'
+        if img_name in all_cases:
+            Image.open(os.path.join(img_file_add, img_name)).resize((512, 512)).save(
+                os.path.join(img_save_add, img_name))
+            if img_name in train_data_id:
+                img_names_list_train.append(img_name)
+                report_list_train.append(report)
+                gt_list_train.append(train_data_gt[np.where(train_data_id==img_name)[0][0]])
+            elif img_name in val_data_id:
+                img_names_list_val.append(img_name)
+                report_list_val.append(report)
+                gt_list_val.append(val_data_gt[np.where(val_data_id == img_name)[0][0]])
+            elif img_name in test_data_id:
+                img_names_list_test.append(img_name)
+                report_list_test.append(report)
+                gt_list_test.append(test_data_gt[np.where(test_data_id == img_name)[0][0]])
+
+datasets = [{"save_add": report_train_save_add,
+              "img_name": np.array(img_names_list_train),
+              "report": np.array(report_list_train),
+              "gt": np.array(gt_list_train)},
+            {"save_add": report_val_save_add,
+             "img_name": np.array(img_names_list_val),
+             "report": np.array(report_list_val),
+             "gt": np.array(gt_list_val)},
+            {"save_add": report_test_save_add,
+             "img_name": np.array(img_names_list_test),
+             "report": np.array(report_list_test),
+             "gt": np.array(gt_list_test)}
+            ]
+for dataset in datasets:
+    create_report(dataset["img_name"], dataset["report"], dataset["gt"], dataset["save_add"])
+
+print('Processed Dataset Files Are Saved !')