object-CXR - Automatic detection of foreign objects on chest X-rays
JF Healthcare

object-CXR (4 files)
dev.csv 136.49kB
dev.zip 1.51GB
train.csv 1.22MB
train.zip 12.13GB
Type: Dataset
Tags:

Bibtex:
@article{,
title= {object-CXR - Automatic detection of foreign objects on chest X-rays},
keywords= {},
author= {JF Healthcare},
abstract= {## Data
5000 frontal chest X-ray images with foreign objects presented and 5000 frontal chest X-ray images without foreign objects were filmed and collected from about 300 township hosiptials in China. 12 medically-trained radiologists with 1 to 3 years of experience annotated all the images. Each annotator manually annotates the potential foreign objects on a given chest X-ray presented within the lung field. Foreign objects were annotated with bounding boxes, bounding ellipses or masks depending on the shape of the objects. Support devices were excluded from annotation. A typical frontal chest X-ray with foreign objects annotated looks like this:

https://i.imgur.com/SFUZy80.jpg


## Annotation

Object-level annotations for each image, which indicate the rough location of each foreign object using a closed shape.

Annotations are provided in csv files and a csv example is shown below.

```csv
image_path,annotation
/path/#####.jpg,ANNO_TYPE_IDX x1 y1 x2 y2;ANNO_TYPE_IDX x1 y1 x2 y2 ... xn yn;...
/path/#####.jpg,
/path/#####.jpg,ANNO_TYPE_IDX x1 y1 x2 y2
...
```

Three type of shapes are used namely rectangle, ellipse and polygon. We use `0`, `1` and `2` as `ANNO_TYPE_IDX` respectively.

- For rectangle and ellipse annotations, we provide the bounding box (upper left and lower right) coordinates in the format `x1 y1 x2 y2` where `x1` < `x2` and `y1` < `y2`.

- For polygon annotations, we provide a sequence of coordinates in the format `x1 y1 x2 y2 ... xn yn`.

> ### Note:
> Our annotations use a Cartesian pixel coordinate system, with the origin (0,0) in the upper left corner. The x coordinate extends from left to right; the y coordinate extends downward.

## Organizers
[JF Healthcare](http://www.jfhealthcare.com/) is the primary organizer of this challenge.
},
terms= {},
license= {https://creativecommons.org/licenses/by-nc/4.0/},
superseded= {},
url= {https://jfhealthcare.github.io/object-CXR/}
}

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Average Time 28 mins, 34 secs
Average Speed 7.96MB/s
Best Time 28 mins, 34 secs
Best Speed 7.96MB/s
Worst Time 28 mins, 34 secs
Worst Speed 7.96MB/s
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