Feb 25, 2019 · Like CXR14, the CheXpert dataset is labelled by natural language processing. This isn’t machine learning based NLP or anything fancy, just traditional “expert systems” (keyword matching with hardcoded rules around negations).
Reports,No Finding,Enlarged Cardiomediastinum,Cardiomegaly,Lung Lesion,Lung Opacity,Edema,Consolidation,Pneumonia,Atelectasis,Pneumothorax,Pleural Effusion,Pleural ...
I am using flow from data frame for a multi-label classification problem with 14 possible labels, all column names are placed in a list in string format for example: columns = ["No Finding", "Enla...
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PadChest (Bustos et al.,2019), CheX aka CheXpert (Irvin et al.,2019), MIMIC-CXR (Johnson et al.,2019), OpenI (Demner-Fushman et al.,2016), Google (Majkowska et al., 2019), Kaggle aka the RSNA Pneumonia Detection Challenge3. Full details of the data are located in Appendix xA. All datasets are manually mapped to 18 common labels.
CheXpert: Chest X-rays CheXpert is a dataset consisting of 224,316 chest radiographs of 65,240 patients who underwent a radiographic examination from Stanford University Medical Center between October 2002 and July 2017, in both inpatient and outpatient centers. Included are their associated radiology reports.
The code for the val section is identical to the ones for the train and test sections and the csv file I'm reading from is the exact same format as the other two csv files. I'm also calling the get_chexpert function for each of them the exact same way.