Dataset Ninja LogoDataset Ninja:

TBX11K Dataset

1170222660
Tagmedical
Taskobject detection
Release YearMade in 2019
LicenseCC BY 4.0
Download4 GB

Introduction #

Released 2019-11-30 ·Yun Liu, Yu-Huan Wu, Yunfeng Banet al.

TBX11K: Tuberculosis X-ray dataset contains 11200 X-ray images with corresponding bounding box annotations for tuberculosis (TB) areas, while the existing largest public TB datasets have much fewer X-ray images with corresponding image-level annotations. All images are with a size of 512x512. There are five main categories in this dataset: healthy, sick_but_non-tb, active_tb, latent_tb, and uncertain_tb. Also, the dataset includes the active_tb&latent_tb categorie. The authors split this dataset into train, val, and test sets, consisting of 6600, 1800, and 2800(3302) X-ray images, respectively. The proposed dataset enables the training of sophisticated detectors for high-quality computer-aided tuberculosis diagnosis (CTD). Authors reform the existing object detectors to adapt them to simultaneous image classification and TB area detection. These reformed detectors are trained and evaluated on the proposed TBX11K dataset and serve as the baselines for future research.

As a serious infectious disease, tuberculosis is one of the major threats to human health worldwide, leading to millions of deaths every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. Computer-aided tuberculosis diagnosis is a promising choice for TB diagnosis due to the great success of deep learning.

Class Train Val Test Total
Non-TB Non-TB Healthy 3000 800 1200 5000
Sick & Non-TB 3000 800 1200 5000
TB Active TB 473 157 294 924
Latent TB 104 36 72 212
Active & Latent TB 23 7 24 54
Uncertain TB 0 0 10 10
Total 6600 1800 2800 11200

active_tb&latent_tb refers to X-rays that contain active and latent TB simultaneously. active_tb and latent_tb refer to X-rays that only contain active TB or latent TB, respectively.

Labels for active_tb:

  • ActiveTuberculosis
    latent_tb:
  • ObsoletePulmonaryTuberculosis
    uncertain_tb refers to TB X-rays whose TB types cannot be recognized under today’s medical conditions. Uncertain TB X-rays are all put into the test set.
tbx11k_preview_1

This is the distribution of the areas of TB bounding boxes. The left and right values of each bin define its corresponding area range, and the height of each bin denotes the number of TB bounding boxes with an area within this range. Note that X-rays are in the resolution of about 3000×3000. However, the original 3000×3000 images will lead to a storage size of over 100GB, which is too large to deliver. On the other hand, the authors found that the resolution of 512 × 512 is enough to train deep models for TB detection and classification. In addition, it is almost impossible to directly use the 3000 × 3000 X-ray images for TB detection due to the limited receptive fields of the existing CNNs. Therefore, the authors decided to only release the X-rays with a resolution of 512×512. For a fair comparison, they recommend all researchers use this resolution for their experiments.

Here are the answers from the dataset’s authors to questions about how the GT labels were created:

  • Are latent TB cases biologically confirmed? (by IFNg testing or tuberculin skin testing)
Yes, they are. Both active and latent TB cases are biologically confirmed
using the hospitals’ accurate clinical diagnosis technology, of course,
in the image level.
  • If the cases are biologically positive for active TB, but does has CXRs regions suspicious for latent TB only, are they labeled as latent TB or active TB?
As clarified in section 3.1.3, the annotation is conducted under a 
double-check rule: “Specifically, each TB X-ray is first labeled by a 
radiologist who has 5-10 years of experience in TB diagnosis. Then, his 
box annotations are further checked by another radiologist who has >10 
years of experience in TB diagnosis. They not only label bounding boxes 
for TB areas but also recognize the TB type (active or latent TB) for 
each box. The labeled TB types are double-checked to make sure that they 
are consistent with the image-level labels produced by the golden 
standard. If a mismatch happens, this X-ray will be put into the 
unlabeled data for re-annotation, and the annotators do not know which 
X-ray was labeled wrong before. If an X-ray is incorrectly labeled twice, 
we will tell the annotators the gold standard of this X-ray and ask them 
to discuss how to re-annotate it.” Therefore, the final TB type must be 
consistent with the golden standard.
  • If the cases are biologically positive for active TB, but does not contain CXRs regions that are not suspicious for active TB, how are they labeled?
The answer is similar to that of the above (2). The image-level labels 
are confirmed using the hospitals’ accurate clinical diagnosis technology 
and thus reliable. In our annotation process, our experienced 
radiologists did not happen to the situation that you said, after 
discussion, the annotation still cannot be consistent with the gold 
standard.
  • How can the cases be both active TB and latent TB?
Of course, one TB region is either active TB or latent TB. However, note 
that an X-ray would contain both active TB and latent TB regions. In our 
dataset, each TB box only has one label of being active or latent, but a 
TB X-ray would have both active TB and latent TB labels.
Datasets Year Class Label Sample
MC 2014 2 Image-level 138
Shenzhen 2014 2 Image-level 662
DA 2014 2 Image-level 156
DB 2014 2 Image-level 150
TBX11K 2020 4 Bounding box 11200

The proposed TBX11K dataset is much larger, better annotated, and more realistic than existing TB datasets, enabling the training of deep CNNs. First, unlike previous (Schenzen dataset and CXR digital image datasets) that only contain several tens/hundreds of X-ray images, TBX11K has 11,200 images that are about 17× larger than the existing largest dataset, i.e., Shenzhen dataset, so that TBX11K makes it possible to train very deep CNNs. Second, instead of only having image-level annotations as in previous datasets, TBX11K annotates TB areas using bounding boxes, so that future CTD methods can not only recognize the manifestations of TB but also detect the TB areas to help radiologists for the definitive diagnosis. Third, TBX11K includes four categories of healthy, active_tb, latent_tb, and sick_but_non-tb, rather than the binary classification for TB or not in previous datasets, so that future CTD systems can adapt to more complex real-world scenarios and provide people with more detailed disease analyses.

The ground truth of the test set will not be released, because it is an online competition for computer-aided tuberculosis diagnosis. To promote the development of this field, the authors suggest you use the train set for training and the val set for validation when tuning your model. When you submit your results of the testing set to the authors’ server, they suggest you train your model on the train+val set and test on the test set.

ExpandExpand
Dataset LinkHomepageDataset LinkResearch Paper

Summary #

TBX11K: Tuberculosis X-ray is a dataset for an object detection task. It is used in the medical research, and in the medical industry.

The dataset consists of 11702 images with 1211 labeled objects belonging to 2 different classes including ActiveTuberculosis and ObsoletePulmonaryTuberculosis.

Images in the TBX11K dataset have bounding box annotations. There are 10903 (93% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (6600 images), test (3302 images), and val (1800 images). Alternatively, the dataset could be split into 6 categories: healthy (3800 images), sick_but_non-tb (3800 images), active_tb (630 images), latent_tb (139 images), active&latent_tb (30 images), and uncertain_tb (0 objects). The dataset was released in 2019 by the Nankai University and InferVision.

Dataset Poster

Explore #

TBX11K dataset has 11702 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 TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
OpenSample annotation mask from TBX11KSample image from TBX11K
👀
Have a look at 11702 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
Images
Objects
Count on image
average
Area on image
average
ActiveTuberculosis
rectangle
660
972
1.47
9.74%
ObsoletePulmonaryTuberculosis
rectangle
169
239
1.41
7.17%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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 1-2 of 2
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
ActiveTuberculosis
rectangle
972
6.62%
26.66%
0.23%
23px
4.49%
364px
71.09%
132px
25.81%
26px
5.08%
212px
41.41%
ObsoletePulmonaryTuberculosis
rectangle
239
5.07%
19.51%
0.28%
24px
4.69%
303px
59.18%
114px
22.22%
23px
4.49%
184px
35.94%

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 1211 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 1211
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
ActiveTuberculosis
rectangle
tb1042.png
512 x 512
63px
12.3%
64px
12.5%
1.54%
2
ActiveTuberculosis
rectangle
tb0680.png
512 x 512
150px
29.3%
145px
28.32%
8.3%
3
ActiveTuberculosis
rectangle
tb0680.png
512 x 512
131px
25.59%
123px
24.02%
6.15%
4
ActiveTuberculosis
rectangle
tb1150.png
512 x 512
83px
16.21%
105px
20.51%
3.32%
5
ActiveTuberculosis
rectangle
tb1150.png
512 x 512
86px
16.8%
101px
19.73%
3.31%
6
ActiveTuberculosis
rectangle
tb1150.png
512 x 512
64px
12.5%
71px
13.87%
1.73%
7
ActiveTuberculosis
rectangle
tb1150.png
512 x 512
53px
10.35%
54px
10.55%
1.09%
8
ActiveTuberculosis
rectangle
tb1168.png
512 x 512
128px
25%
163px
31.84%
7.96%
9
ActiveTuberculosis
rectangle
tb1028.png
512 x 512
76px
14.84%
77px
15.04%
2.23%
10
ActiveTuberculosis
rectangle
tb0564.png
512 x 512
98px
19.14%
117px
22.85%
4.37%

License #

Tuberculosis X-ray is under CC BY 4.0 license.

Source

Citation #

If you make use of the TBX11K data, please cite the following reference:

@inproceedings{liu2020rethinking,
  title={Rethinking computer-aided tuberculosis diagnosis},
  author={Liu, Yun and Wu, Yu-Huan and Ban, Yunfeng and Wang, Huifang and Cheng, Ming-Ming},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2646--2655},
  year={2020}
}

Source

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

@misc{ visualization-tools-for-tbx-11k-dataset,
  title = { Visualization Tools for TBX11K Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/tbx-11k } },
  url = { https://datasetninja.com/tbx-11k },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { may },
  note = { visited on 2024-05-26 },
}

Download #

Dataset TBX11K can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='TBX11K', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

. . .

Disclaimer #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.