Chest X-Ray Images Pneumonia

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on Chest X-Ray Images Pneumonia

Chest X-Ray Images (Pneumonia)

This dataset contains X-Ray images of patients suffering from Pneumonia in comparison with X-Ray images referring to normal condition. For more information please refer to https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/home.The data files can be downloaded separately for training, testing and validation available on Kaggle https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia.

Here is some information regarding this dataset:

  • Number of images in the dataset: 5863 images (5216 images for training, 624 images for test and 16 images for validation)

  • Number of classes: 2 (Normal or Pneumonia)

  • Image resolution is different for the image samples.

If you use this dataset:

Please make sure to read the License carefully which is available on https://creativecommons.org/licenses/by/4.0/.

Please make sure to cite the paper:

D. S. Kermany, M. Goldbaum, W. Cai, et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 2018.

keywords: Vision, Image, Biology and Health, X-Ray, Classification

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HAM10000

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on HAM10000

HAM10000:

This dataset contains 10015 dermatoscopic images of pigmented lesions for patients in 7 diagnostic categories. For more than half of the subjects, the diagnosis was confirmed through histopathology and for the rest of the patience through follow-up examinations, expert consensus, or by in-vivo confocal microscopy. More information about the dataset and the diagnosis categories, features and patience conditions besides the links to download the dataset can be found on either Harvard Dataverse https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T or on Kaggle https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000/home. This dataset is for non-commercial use only.

Here is some information regarding the dataset:

Number of Images: 10015 dermatoscopic images

Number of categories: 7 diagnostic categories of pigmented lesions

If you use this dataset:

Make sure to read the Terms of Use carefully, which is available on the same page and needs confirmation before downloading the data files. This dataset is for non-commercial use only.

Make sure to cite the dataset:

Tschandl, Philipp, 2018, The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, https://doi.org/10.7910/DVN/DBW86T, Harvard Dataverse, V1, UNF:6:IQTf5Cb+3EzwZ95U5r0hnQ== [fileUNF]

keywords: Vision, Image, Biology and Health, Classification

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CBIS-DDSM

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on CBIS-DDSM

CBIS-DDSM: Curated Breast Imaging Subset of DDSM:

This dataset contains images for screening Mammography and is a subset of a DDSM dataset (Digital Database for Screening Mammography http://marathon.csee.usf.edu/Mammography/Database.html). CBIS-DDSM contains images of cases with three conditions of breast cancer (normal, benign, and malignant). The dataset also includes ROI segmentation and bounding boxes and pathologic diagnosis for the training data. This dataset can be downloaded from the Data Access section on https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM#97542eefbc8e4234a95231cbcd86cb1d.

Here is some information regarding this dataset:

  • Number of images in the dataset: 10,239

  • Number of subjects: 6671

  • Total Images Size in GB: 163.6

If you use this dataset:

Make sure to cite these papers:

R. S. Lee, F. Gimenez, A. Hoogi, D. Rubin. Curated Breast Imaging Subset of DDSM. The Cancer Imaging Archive, 2016.

R. S. Lee, F. Gimenez, A. Hoogi, K. K. Miyake, M. Gorovoy, D. L. Rubin. A Curated Mammography Data set for Use in Computer-aided Detection and Diagnosis Research. Scientific Data Volume 4, Article number: 170177, 2017.

K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, F. Prior. The Cancer Imaging Archive(TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, 2013.

Make sure to follow the Policy and Terms of Use available on https://creativecommons.org/licenses/by/3.0/ and https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions.

keywords: Vision, Image, Biology and Health, CT, Classification, Cancer

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NLST: National Lung Screening Trial

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on NLST: National Lung Screening Trial

NLST: National Lung Screening Trial:

This dataset contains images of the screening tests of patients suffering from lung cancer collected during a controlled clinical trial. The patients participated in a study for about 6.5 years of follow-up, while they were randomly divided into two groups of either receiving a low-dose helical CT screening or a single-view chest radiography. The dataset is not public, and a research proposal is required to gain access and download the dataset. To obtain more information regarding the research details or to request to gain access to the dataset, please refer to https://wiki.cancerimagingarchive.net/display/NLST/National+Lung+Screening+Trial#4c242d6186bf4aff949bb62cb2ab60da or https://biometry.nci.nih.gov/cdas/learn/nlst/images/. Additionally, a detailed description regarding the dataset participants, CT screening and abnormalities, X-Ray screening and abnormalities, diagnostic procedures, treatment, cause of death and so many other useful information about the dataset is available on https://biometry.nci.nih.gov/cdas/datasets/nlst/.

Here is some information regarding this dataset:

  • Number of images in the dataset: 21,082,502

  • Number of subjects: 26,254

  • Total Images Size in TB: 11.3

If you use this dataset:

Make sure to provide proper citations according to the Citations & Data Usage Policy available on the same page provided above.

Make sure to follow the Policy and Terms of Use even after receiving access to use the dataset for your own research purpose https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions.

keywords: Vision, Image, Biology and Health, CT, Classification, Cancer

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Human Protein Atlas Image

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on Human Protein Atlas Image

Human Protein Atlas Image:

This dataset contains protein images of human body available from the Human Protein Atlas Image Classification Competition on Kaggle or from The Human Protein Atlas page https://www.proteinatlas.org/cell. The dataset might be either used for the Kaggle Competition, research and education and non-commercial purposes. Please refer to the competition rules on Kaggle for more information about the Terms of Use and the Rules regarding the dataset https://www.kaggle.com/c/human-protein-atlas-image-classification/rules.

Here is some information regarding this dataset:

  • Number of classes: 28 categories as integers from 0 to 27, each referring to a human protein.

  • Available separate datafiles for training and testing with three resolutions: 512×512 PNG, 2048×2048 TIFF, 3072×3072 TIFF

If you use this dataset:

Make sure to use the dataset for non-commercial purposes only.

keywords: Vision, Image, Biology and Health, Classification, Protein, Cell, Object Detection

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COIL-100

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on COIL-100

COIL-100:

This dataset contains color images of objects at every 5 angles in a 360 degree rotation. The dataset was collected by the Center for Research on Intelligent Systems at the Department of Computer Science, Columbia University. This dataset was used in a real-time image recognition study.

Here is some information regarding this dataset:

  • Number of images in the dataset: 7200 images

  • Number of classes: 100 object categories each with 72 poses

  • Image resolution: 128×128

More information can be found in the technical report in bellow, or the Kaggle page https://www.kaggle.com/jessicali9530/coil100/home.

The main page for the dataset can be found on http://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php.

If you use this dataset:

Please make sure to use the dataset for non-commercial research purposes only (Terms of Use).

Please refer to the technical report in bellow and cite:

S. A. Nene, S. K. Nayar and H. Murase, Columbia Object Image Library (COIL-100), Technical Report CUCS-006-96, February 1996.

keywords: Vision, Image, Classification, Object Detection, Rotation

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WIDER FACE

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on WIDER FACE

WIDER FACE:

This dataset which is a subset of WIDER dataset contains labeled face images with different poses, scales and different situations like marching or hand shaking. Separate download links are available on the dataset page for training, validation and testing with random selection of 40%, 10% and 50% of the whole data respectively. The evaluation and testing results are available for comparison on the results section of the page. You can find this information as well as the download links on http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/index.html.

Here is some information regarding this dataset:

  • Number of images in the dataset: 32,203 images

  • Number of identities: 393,703 subjects with labeled faces

  • Image resolution: 1024×754

If you use this dataset:

Please make sure to cite the paper:

S. Yang, P. Luo, C. C. Loy, X. Tang, WIDER FACE: A Face Detection Benchmark. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

keywords: Vision, Image, Face, Face Detection, Classification, Event

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CelebA: Large-scale CelebFaces Attributes

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on CelebA: Large-scale CelebFaces Attributes

CelebA: Large-scale CelebFaces Attributes:

This dataset contains color face images with 40 attribute annotations for each image. The dataset can be used for different computer vision tasks including face detection, face attribute recognition and landmark or facial part localization. More information about the dataset and links of download can be found on the dataset page http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html or the Kaggle page https://www.kaggle.com/jessicali9530/celeba-dataset/version/2.

Here is some information regarding this dataset:

  • Number of images in the dataset: 202,599 images

  • Number of identities: 10,177 subjects

  • Image resolution: 178×218

If you use this dataset:

Please make sure to use the dataset for non-commercial research purposes only (Terms of Use).

Please make sure to cite the paper:

S. Yang, P. Luo, C. C. Loy, X. Tang, From Facial Parts Responses to Face Detection: A Deep Learning Approach. IEEE International Conference on Computer Vision (ICCV), 2015.

keywords: Vision, Image, Face, Celeb Faces, Face Recognition

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AgeDB

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on AgeDB

AgeDB:

This dataset contains face images of celebrities, politicians and scientists in different ages and poses. The annotations per image include gender, age and identity of the person in the image. The age variations are from 3 to 101 years old. In the paper mentioned bellow, they have used AgeDB dataset for different experiments including age estimation, age invariant face verification and face age progression. The link for download can be found on https://ibug.doc.ic.ac.uk/resources/agedb/.

Here is some information regarding this dataset:

  • Number of images in the dataset: 12,240 images

  • Number of identities: 440 subjects

If you use this dataset:

Please make sure to use the dataset for non-commercial research purposes only (Terms of Use). The detailed Terms of Use can be found on https://ibug.doc.ic.ac.uk/resources/agedb/.

Please make sure to cite the paper:

S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia, S. Zafeiriou. AgeDB: The First Manually Collected, In-the-wild Age Dataset. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR-W), 2017.

keywords: Vision, Image, Face, Age Estimation, Face Verification, Celeb Faces

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LFW: Labeled Faces in the Wild

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on LFW: Labeled Faces in the Wild

LFW: Labeled Faces in the Wild:

This dataset contains labeled face images collected from the web with names of the people in the images as the labels. Some of these people have two or more number of images in the dataset. This dataset is designed for studying the problem of unconstrained face recognition and face verification. The original LFW dataset is available for download along with 3 sets of aligned images (funneled images, LFW-a, deep funneled).

Here is some information regarding this dataset:

  • Number of images in the dataset: 13,000 images (10-fold cross validation is recommended and training and test splits can be downloaded from the dataset page)

  • Number of identities: 5749

  • Image resolution: 250×250

More details and links for download can be found on the dataset page http://vis-www.cs.umass.edu/lfw/.

If you use the any of these versions of the LFW image dataset:

Please make sure to cite the paper:

G. B. Huang, M. Ramesh, T. Berg, E. Learned-Miller, Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49, October 2007.

If you use the LFW imaged aligned by deep funneling:

Please make sure to cite the paper:

G. B. Huang, M. Matter, H. Lee, E. Learned-Miller, Learning to Align from Scratch. Advances in Neural Information Processing Systems (NIPS), 2012.

If you use the LFW imaged aligned by funneling:

Please make sure to cite the paper:

G. B. Huang, V. Jain, E. Learned-Miller, Unsupervised Joint Alignment of Complex Images. International Conference on Computer Vision (ICCV), 2007.

keywords: Vision, Image, Face, Object Detection, Segmentation, In the Wild

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