RSNA Pneumonia Detection Challenge (DICOM files)

@article{,
title= {RSNA Pneumonia Detection Challenge (DICOM files)},
keywords= {},
author= {},
abstract= {Details from the challenge:

## What am I predicting?

In this challenge competitors are predicting whether pneumonia exists in a given image. They do so by predicting bounding boxes around areas of the lung. Samples without bounding boxes are negative and contain no definitive evidence of pneumonia. Samples with bounding boxes indicate evidence of pneumonia.

When making predictions, competitors should predict as many bounding boxes as they feel are necessary, in the format: confidence x-min y-min width height

There should be only ONE predicted row per image. This row may include multiple bounding boxes.

A properly formatted row may look like any of the following.

For patientIds with no predicted pneumonia / bounding boxes: 0004cfab-14fd-4e49-80ba-63a80b6bddd6,

For patientIds with a single predicted bounding box: 0004cfab-14fd-4e49-80ba-63a80b6bddd6,0.5 0 0 100 100

For patientIds with multiple predicted bounding boxes: 0004cfab-14fd-4e49-80ba-63a80b6bddd6,0.5 0 0 100 100 0.5 0 0 100 100, etc.

## File descriptions
```
stage_2_train.csv - the training set. Contains patientIds and bounding box / target information.
stage_2_detailed_class_info.csv - provides detailed information about the type of positive or negative class for each image.
```
## Data fields
```
patientId _- A patientId. Each patientId corresponds to a unique image.
x_ - the upper-left x coordinate of the bounding box.
y_ - the upper-left y coordinate of the bounding box.
width_ - the width of the bounding box.
height_ - the height of the bounding box.
Target_ - the binary Target, indicating whether this sample has evidence of pneumonia.
```},
terms= {},
license= {},
superseded= {},
url= {https://www.kaggle.com/c/rsna-pneumonia-detection-challenge}
}