# Data Science Bowl 2017 — Data

来源：https://www.kaggle.com/competitions/data-science-bowl-2017/data

采集时间：2026-10-24T20:49:13Z

NOTE: due to data set usage restrictions, the data for this competition is no longer available for download.
In this dataset, you are given over a thousand low-dose CT images from high-risk patients in
[DICOM](https://en.wikipedia.org/wiki/DICOM)
format. Each image contains a series with multiple axial slices of the chest cavity. Each image has a variable number of 2D slices, which can vary based on the machine taking the scan and patient.
The DICOM files have a header that contains the necessary information about the patient id, as well as scan parameters such as the slice thickness.
![](assets/1acca59acf663339308504db.png)
The competition task is to create an automated method capable of determining whether or not the patient will be diagnosed with lung cancer within one year of the date the scan was taken. The ground truth labels were confirmed by pathology diagnosis.
The images in this dataset come from many sources and will vary in quality. For example, older scans were imaged with less sophisticated equipment. You should expect the stage 2 data to be, on the whole, more recent and higher quality than the stage 1 data (generally having thinner slice thickness). Ideally, your algorithm should perform well across a range of image quality.
Notes
Use of external data is permitted in this competition, provided the data is freely available. If you are using a source of external data, you must post the source to the official external data forum thread no later than one week prior to the deadline of the first stage.
This is a two-stage competition. In order to appear on the final competition leaderboard and receive ranking points, your team must make a submission during both stages of the competition.
Due to the large file size,
Kaggle is beta testing use of BitTorrent as an alternate means of download
. The image archives are encrypted in order to prevent outside access. Please do not share the decryption password. The large stage1.7z archive hosted on BitTorrent is the same as the version available for direct download.
File Descriptions
Each patient id has an associated directory of DICOM files. The patient id is found in the DICOM header and is identical to the patient name. The exact number of images will differ from case to case, varying according in the number of slices. Images were compressed as .7z files due to the large size of the dataset.
stage1.7z
- contains all images for the first stage of the competition, including both the training and test set. This is file is also hosted on BitTorrent.
stage1_labels.csv
- contains the cancer ground truth for the stage 1 training set images
stage1_sample_submission.csv
- shows the submission format for stage 1. You should also use this file to determine which patients belong to the leaderboard set of stage 1.
sample_images.7z
- a smaller subset set of the full dataset, provided for people who wish to preview the images before downloading the large file.
data_password.txt
- contains the decryption key for the image files
The DICOM standard is complex and there are a number of different tools to work with DICOM files. You may find the following resources helpful for managing the competition data:
The
[lite version of OsiriX](http://www.osirix-viewer.com/)
is useful for viewing images on OSX.
[pydicom](https://pydicom.readthedocs.org/en/latest/)
: A package for working with images in python.
[oro.dicom](https://cran.r-project.org/web/packages/oro.dicom/index.html)
: A package for working with images in R.
[Mango](http://www.idoimaging.com/program/352)
: A useful DICOM viewer for Windows users.