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  "fetched_at": "2026-10-19T06:28:46Z",
  "source_url": "https://www.kaggle.com/competitions/amia-public-challenge-2024/data",
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      "content": "In this competition, we are classifying common thoracic lung diseases and localizing critical findings. This is an object detection and classification problem.\n\nFor each test image, you will be predicting a bounding box and class for all findings. If you predict that there are no findings, you should create a prediction of \"14 1 0 0 1 1\" (14 is the class ID for no finding, and this provides a one-pixel bounding box with a confidence of 1.0).\n\nThe images are in PNG format (already converted from DICOM for you) and resized to 1024x1024 pixels.\n\n## Dataset information\nThe dataset comprises 15,000 postero-anterior (PA) CXR scans in PNG format.\nAll images were labeled by a panel of experienced radiologists for the presence of 14 critical radiographic findings as listed below:\n\n```\n0 - Aortic enlargement\n1 - Atelectasis\n2 - Calcification\n3 - Cardiomegaly\n4 - Consolidation\n5 - ILD\n6 - Infiltration\n7 - Lung Opacity\n8 - Nodule/Mass\n9 - Other lesion\n10 - Pleural effusion\n11 - Pleural thickening\n12 - Pneumothorax\n13 - Pulmonary fibrosis\n```\nThe \"No finding\" observation (14) was intended to capture the absence of all findings above.\n\nNote that a key part of this competition is working with ground truth from multiple radiologists.\n\n## Files\n* **train.csv** - the train set metadata, with one row for each object, including a class and a bounding box. Some images in both test and train have multiple objects (several rows with respect the same image).\n\n* **test.csv** - the test set metadata, only containing the filenames.\n* **img_size.csv** - the original dimensions of the files: dim0 is the height and dim1 is the width.\n* **sample_submission.csv** - sample submission (similar to test.csv) with dummy results. \n\n## Columns\n```\nimage_id - unique image identifier\nclass_name - the name of the class of detected object (or \"No finding\")\nclass_id - the ID of the class of detected object\nrad_id - the ID of the radiologist that made the observation\nx_min - minimum X coordinate of the object's bounding box\ny_min - minimum Y coordinate of the object's bounding box\nx_max - maximum X coordinate of the object's bounding box\ny_max - maximum Y coordinate of the object's bounding box\n```",
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    {
      "name": "Description",
      "content": "In this competition, you have to automatically localize and classify 14 types of thoracic abnormalities from chest radiographs. The subset is based on a dataset consisting of a total of 15,000 scans that have been annotated by experienced radiologists. From those, 8,573 images have been used for training and 6,427 images for testing.  The train csv contains 45,925 rows and the test csv 21,989. \n\nDetails on building the dataset can be found in our recent paper “VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations”.",
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      "name": "Evaluation",
      "content": "The challenge uses the standard PASCAL VOC 2010 mean Average Precision (mAP) at IoU > 0.4.\n\n## Submission File\nImages in the test set may contain more than one object. For each object in a given test image, you must predict a class ID, confidence score, and bounding box in format xmin ymin xmax ymax. If you predict that there are NO objects in a given image, you should predict 14 1.0 0 0 1 1, where 14 is the class ID for \"No finding\", 1.0 is the confidence, and 0 0 1 1 is a one-pixel bounding box.\n\nThe submission file should contain a header and have the following format:\n```\nID,TARGET\n004f33259ee4aef671c2b95d54e4be68,14 1 0 0 1 1\n004f33259ee4aef671c2b95d54e4be69,11 0.5 100 100 200 200 13 0.7 10 10 20 20\netc.\n```",
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    {
      "name": "rules",
      "content": "The Rules for AMIA challenge. \nProvide classification for each image according to the classes of the challenge. ",
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    {
      "name": "abstract",
      "content": "Challenge based on a sub-set of the Ving Big Data Chest X-ray Challenge \n\nhttps://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/\n",
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