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

folder object-CXR (4 files)
filedev.csv 136.49kB
filedev.zip 1.51GB
filetrain.csv 1.22MB
filetrain.zip 12.13GB
Type: Dataset
Tags: radiology

Metadata:
@article{,
title= {Object-CXR - Automatic detection of foreign objects on chest X-rays},
keywords= {radiology},
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superseded= {},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://web.archive.org/web/20201127235812/https://jfhealthcare.github.io/object-CXR/}
}

Citation:
Healthcare, J.. (2020). Object-CXR - Automatic detection of foreign objects on chest X-rays [Data set]. Academic Torrents. https://academictorrents.com/details/fdc91f11d7010f7259a05403fc9d00079a09f5d5
Hosted by users

Send Feedback Start
   0.000009
DB Connect
   0.000881
Lookup hash in DB
   0.000750
Get torrent details
   0.000267
Get torrent details, finished
   0.001038
Get authors
   0.000051
Parse bibtex
   0.000316
Write header
   0.000466
get stars
   0.000221
home tab
   0.000510
render right panel
   0.000008
render ads
   0.000907
fetch current hosters
   0.000458
related datasets
   0.012735
Done