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DeepLesion Dataset

3211811750
Tagmedical
Taskobject detection
Release YearMade in 2018
Licenseunknown

Introduction #

Ke Yan, Xiaosong Wang, Le Luet al.

The authors of the DeepLesion dataset recognize the importance and challenges of extracting, harvesting, and constructing large-scale annotated radiological image datasets. They propose a scalable approach that involves mining clinical annotations, specifically bookmarks in picture archiving and communication systems (PACS), made by radiologists during their daily workflow. This process results in the creation of DeepLesion, a dataset containing 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients, covering various lesion types such as lung nodules, liver tumors, and enlarged lymph nodes. The authors emphasize the dataset’s potential applications in medical image tasks and use it to train a universal lesion detector with a unified framework. The full 220GB dataset with unlabeled images could be downloaded here.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkKaggle

Summary #

DeepLesion is a dataset for object detection, unsupervised learning, and weakly supervised learning tasks. It is used in the medical industry.

The dataset consists of 32118 images with 32733 labeled objects belonging to 1 single class (lesion).

Images in the DeepLesion dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (22494 images), test (4831 images), and val (4793 images). Additionally, every image contains information about age and gender, while every object contains information about lesion diameter and lesion type. The dataset was released in 2018 by the National Institutes of Health, USA.

Dataset Poster

Explore #

DeepLesion dataset has 32118 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
OpenSample annotation mask from DeepLesionSample image from DeepLesion
👀
Have a look at 32118 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-1 of 1
Class
Images
Objects
Count on image
average
Area on image
average
lesion
rectangle
32118
32733
1.02
0.65%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-1 of 1
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
lesion
rectangle
32733
0.64%
31.27%
0.05%
11px
2.08%
307px
59.96%
35px
6.89%
11px
2.15%
372px
72.66%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 32733 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 32733
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
lesion
rectangle
002358_01_01_069.png
512 x 512
31px
6.05%
31px
6.05%
0.37%
2
lesion
rectangle
000161_04_01_060.png
512 x 512
29px
5.66%
27px
5.27%
0.3%
3
lesion
rectangle
001532_04_01_049.png
512 x 512
31px
6.05%
47px
9.18%
0.56%
4
lesion
rectangle
003204_01_01_059.png
512 x 512
19px
3.71%
22px
4.3%
0.16%
5
lesion
rectangle
002488_03_01_026.png
512 x 512
38px
7.42%
33px
6.45%
0.48%
6
lesion
rectangle
001532_03_01_050.png
512 x 512
22px
4.3%
20px
3.91%
0.17%
7
lesion
rectangle
000254_05_01_103.png
512 x 512
30px
5.86%
40px
7.81%
0.46%
8
lesion
rectangle
000732_10_02_309.png
512 x 512
31px
6.05%
32px
6.25%
0.38%
9
lesion
rectangle
003458_01_01_025.png
512 x 512
23px
4.49%
22px
4.3%
0.19%
10
lesion
rectangle
000655_01_02_121.png
512 x 512
32px
6.25%
30px
5.86%
0.37%

License #

License is unknown for the DeepLesion dataset.

Source

Citation #

If you make use of the DeepLesion data, please cite the following reference:

@dataset{DeepLesion,
  author={Ke Yan and Xiaosong Wang and Le Lu and Ronald M. Summers},
  title={DeepLesion},
  year={2018},
  url={https://nihcc.app.box.com/v/DeepLesion/}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-deep-lesion-dataset,
  title = { Visualization Tools for DeepLesion Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/deep-lesion } },
  url = { https://datasetninja.com/deep-lesion },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Please visit dataset homepage to download the data.

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