DeepLesion - Dataset Ninja

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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Research Paper

Kaggle

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.

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.

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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.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	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.

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.

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.

Rows 1-10 of 32733

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
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 = { 2025 },
  month = { jan },
  note = { visited on 2025-01-15 },
}

Download #

Please visit dataset homepage to download the data.

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Disclaimer #

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