This dataset is about book series. It has 1 row and is filtered where the books is Women of the galaxy. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about politicians. It has 13,407 rows and is filtered where the gender is female. It features 10 columns including birth date, death date, country, and gender.

===###Overview:This repository provides high-utility synthetic survival datasets generated using the CK4Gen framework, optimised to retain critical clinical characteristics for use in research and educational settings. Each dataset is based on a carefully curated ground truth dataset, processed with standardised variable definitions and analytical approaches, ensuring a consistent baseline for survival analysis.###===###Description:The repository includes synthetic versions of four widely utilised and publicly accessible survival analysis datasets, each anchored in foundational studies and aligned with established ground truth variations to support robust clinical research and training.#---GBSG2: Based on Schumacher et al. [1]. The study evaluated the effects of hormonal treatment and chemotherapy duration in node-positive breast cancer patients, tracking recurrence-free and overall survival among 686 women over a median of 5 years. Our synthetic version is derived from a variation of the GBSG2 dataset available in the lifelines package [2], formatted to match the descriptions in Sauerbrei et al. [3], which we treat as the ground truth.ACTG320: Based on Hammer et al. [4]. The study investigates the impact of adding the protease inhibitor indinavir to a standard two-drug regimen for HIV-1 treatment. The original clinical trial involved 1,151 patients with prior zidovudine exposure and low CD4 cell counts, tracking outcomes over a median follow-up of 38 weeks. Our synthetic dataset is derived from a variation of the ACTG320 dataset available in the sksurv package [5], which we treat as the ground truth dataset.WHAS500: Based on Goldberg et al. [6]. The study follows 500 patients to investigate survival rates following acute myocardial infarction (MI), capturing a range of factors influencing MI incidence and outcomes. Our synthetic data replicates a ground truth variation from the sksurv package, which we treat as the ground truth dataset.FLChain: Based on Dispenzieri et al. [7]. The study assesses the prognostic relevance of serum immunoglobulin free light chains (FLCs) for overall survival in a large cohort of 15,859 participants. Our synthetic version is based on a variation available in the sksurv package, which we treat as the ground truth dataset.###===###Notes:Please find an in-depth discussion on these datasets, as well as their generation process, in the link below, to our paper:https://arxiv.org/abs/2410.16872Kuo, et al. "CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare." arXiv preprint arXiv:2410.16872 (2024).###===###References:[1]: Schumacher, et al. “Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. German breast cancer study group.”, Journal of Clinical Oncology, 1994.[2]: Davidson-Pilon “lifelines: Survival Analysis in Python”, Journal of Open Source Software, 2019.[3]: Sauerbrei, et al. “Modelling the effects of standard prognostic factors in node-positive breast cancer”, British Journal of Cancer, 1999.[4]: Hammer, et al. “A controlled trial of two nucleoside analogues plus indinavir in persons with human immunodeficiency virus infection and cd4 cell counts of 200 per cubic millimeter or less”, New England Journal of Medicine, 1997.[5]: Pölsterl “scikit-survival: A library for time-to-event analysis built on top of scikit-learn”, Journal of Machine Learning Research, 2020.[6]: Goldberg, et al. “Incidence and case fatality rates of acute myocardial infarction (1975–1984): the Worcester heart attack study”, American Heart Journal, 1988.[7]: Dispenzieri, et al. “Use of nonclonal serum immunoglobulin free light chains to predict overall survival in the general population”, in Mayo Clinic Proceedings, 2012.

Feed the Future Rwanda Interim Survey in the Zone of Influence: This dataset (n=17,964, vars=112) is the second of two datasets needed to calculate the WEAI-related measures. It includes the 24-hour time allocation data from Module G6, the time use module, and thus each respondent on Module G has multiple records, one for each of the 18 time use activities (998 respondents x 18 activities = 17,964 records.)

This data set serves as a resource for correlating hand x-rays with bone mineral density (BMD) scans taken within one year of one another. The need for increased methods of screening for low BMD is needed. Therefore, we used this dataset to determine if hand and wrist x-rays could be used to screen for forearm osteopenia and osteoporosis. Methods DXAs: DXA scans are of the hip, spine, and wrists. There were prospective participants that had DXAs on a GE Healthcare Lunar iDXA scanner (GE Healthcare, Chicago, Illinois, USA) with enCORE software Version 16 (GE Healthcare, Chicago, Illinois, USA), retrospective chart review participants' DXAs were taken both on a GE Lunar DXA scanner and Hologic Horizon scanner (Hologic Inc., Bedford, MA, USA) with APEX software version 5.6.0.5 (Hologic Inc., Bedford, MA, USA). BMD and T-scores were calculated for the following locations: total AP spine (L1, L2, L3, L4, L1-L4), femoral neck (left and right), femoral trochanter (left and right), total hip (left and right), 1/3 distal forearm (left and right), most distal forearm (left and right), and total forearm (left and right). In one case, total AP spine was taken from L1-L3 instead of L1-L4 due to technical difficulties. Another patient did not have values from the right femur due to a prior fracture. Cortical Percentage: The PA view of the available hand or wrist x-rays was uploaded into ImageJ for image processing. The mid-diaphysis of the second metacarpal was localized with the magnification function to optimize measurement. The observer chose the isthmus as the site along the second metacarpal by visually assessing the narrowest part of the cortex. The measurement tool was then used to measure the diameter of the second metacarpal at the isthmus (portion A). The second measurement was made parallel to this, at the same location, and only included the intramedullary component (portion B). We then calculated the cortical percentage by the following formula [(A-B)/A]x100(21). Measurements were confirmed by two independent raters. Other data included: participants' age, hand dominance, and BMI (categorized into bins).

Feed the Future Nepal Interim Survey in the Zone of Influence: This dataset (n=14,400, vars=113) is the second of two datasets needed to calculate the WEAI-related measures. It includes the 24-hour time allocation data from Module G6, the time use module, and thus each respondent on Module G has multiple records, one for each of the 18 time use activities (800 respondents x 18 activities = 14,400 records.)

Speaker embeddings extracted from CMU ARCTIC

There is one .npy file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors. The CMU ARCTIC dataset divides the utterances among the following speakers:

bdl (US male) slt (US female) jmk (Canadian male) awb (Scottish male) rms (US male) clb (US female) ksp (Indian male)

The X-vectors were extracted using this script, which uses the speechbrain/spkrec-xvect-voxceleb model. Usage:… See the full description on the dataset page: https://huggingface.co/datasets/Dupaja/cmu-arctic-xvectors.

The selfie dataset contains 46,836 selfie images annotated with 36 different attributes. We only use photos of females as training data and test data. The size of the training dataset is 3400, and that of the test dataset is 100, with the image size of 256 x 256. For the anime dataset, we have firstly retrieved 69,926 animation character images from Anime-Planet1. Among those images, 27,023 face images are extracted by using an anime-face detector2. After selecting only female character images and removing monochrome images manually, we have collected two datasets of female anime face images, with the sizes of 3400 and 100 for training and test data respectively, which is the same numbers as the selfie dataset. Finally, all anime face images are resized to 256 x 256 by applying a CNN-based image super-resolution algorithm.

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This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset is a remake of the famous GunPoint dataset released in 2003. We strive to mimic in every aspect the recording of the original GunPoint. The actors include one male and one female. They are the same actors who created the original GunPoint. We record two scenarios, Gun and Point (also known as Gun and NoGun). In each scenario, the actors aim at a eye-level target. The difference between Gun and Point is that for the Gun scenario, the actors hold a gun, and in the Point scenario, the actors point with just their fingers. A complete Gun action involves the actor moves hand from an initial rest position, points the gun at target, puts gun back to waist holster and then brings free hand to the initial rest position. Each complete action conforms to a five-second cycle. With 30fps, this translates into 150 frames per action. We extract the centroid of the hand from each frame and use its x-axis coordinate to form a time series. We refer to the old GunPoint as GunPoint 2003 and the new GunPoint as Gunpoint 2018. We merged GunPoint 2003 and GunPoint 2018 to make three datasets. Let us denote: - G: Gun - P: Point - M: Male - F: Female - 03: The year 2003 - 18: The year 2018 ## GunPointAgeSpan The task is to classify Gun and Point. There are 4 flavors of each class. - Class 1: Gun (FG03, MG03, FG18, MG18) - Class 2: Point (FP03, MP03, FP18, MP18) ## GunPointMaleVersusFemale The task is to classify Male and Female. There are 4 flavors of each class. - Class 1: Female (FG03, FP03, FG18, FP18) - Class 2: Male (MG03, MP03, MG18, MP18) ## GunPointOldVersusYoung The task is to classify the older and younger version of the actors. There are 4 flavors of each class. - Class 1: Young (FG03, MG03, FP03, MP03) - Class 2: Old (FG18, MG18, FP18, MP18) There is nothing to infer from the order of examples in the train and test set. Data created by Ann Ratanamahatana and Eamonn Keogh. Data edited by Hoang Anh Dau.

Donator: A. Ratanamahatana, E. Keogh

The Model Clothing Segmentation Dataset is curated for the e-commerce & retail sector, featuring a collection of internet-collected images with a resolution of 816 x 1224 pixels. This dataset focuses on semantic segmentation of high-resolution images showcasing models in various outfits, encompassing male, female, and children's wear, to accurately reflect real human silhouettes. The annotations include detailed segmentation of the clothing worn by the models, such as hats, shoes, tops, and bottoms.

Introduction

Magnetic Resonance Imaging (MRI) is widely recommended as a primary non-invasive diagnostic tool for endometriosis. Endometriomas affect 17–44% of women diagnosed with the condition. Accurate MRI-based ovary segmentation in endometriosis patients is essential for detecting endometriomas, guiding surgery, and predicting post-operative complications. However, ovary segmentation becomes challenging when the ovary is deformed or absent, often due to surgical resection, emphasizing the need for highly experienced clinicians. An automatic segmentation pipeline for pelvic MRI in endometriosis patients could greatly reduce the manual workload for clinicians and help standardize ovary segmentation.

The UTHealth Endometriosis MRI Dataset (UT-EndoMRI) includes multi-sequence MRI scans and structural labels collected from two clinical institutions, Memorial Hermann Hospital System and Texas Children’s Hospital Pavilion for Women. The first dataset comprises MRI scans and labels from 51 patients collected before 2022, featuring T2-weighted and T1-weighted fat-suppressed MRI sequences. The uterus, ovaries, endometriomas, cysts, and cul-de-sac structures were manually segmented by three raters. The second dataset, collected in 2022, consists of MRI scans and labels from 82 endometriosis patients. These sequences include T1-weighted, T1-weighted fat suppression, T2-weighted, and T2-weighted fat suppression MRI. In this dataset, the uterus, ovaries, and endometriomas were manually contoured by a single rater. Using these datasets, we investigated interrater agreement and developed an automatic ovary segmentation pipeline, RAovSeg, for endometriosis.

The study and the data sharing were approved by the Committee for the Protection of Human Subjects at UTHealth (protocol no. HSC-SBMI-22-0184). The UT-EndoMRI dataset is available for free use exclusively in non-commercial scientific research.

Endometriosis MRI

This dataset includes MRI scans and labels from two clinical institutions. The data from the first institution can be found in the ```D1_MHS/ ```directory, while the data from the second institution are located in the ```D2_TCPW/``` directory. Each subfolder contains MRI scans and corresponding labels from different raters.

The naming conventions for the files are as follows:

MRI scans:
D[dataset ID]- [patient ID] _ [MRI sequence].nii.gz

Anatomical structure labels:
D[dataset ID]- [patient ID] _ [structure name] _ r[rater ID].nii.gz

For the labels in the ```D2_TCPW/ ```directory, since they were generated by a single rater, there is no rater ID included in the file names.

The abbreviations used for naming:
T1: T1-weighted MRI
T1FS: T1-weighted fat suppression MRI
T2: T2-weighted MRI
T2FS: T2-weighted fat suppression MRI
ov: ovary
ut: uterus
em: endometrioma
cy: cyst
cds: cul de sac

For example, the file located at ```UT-EndoMRI/D1_MHS/D1-000/D1-000_T1FS.nii.gz```represents the T1 weighted fat suppression MRI for subject 000 in dataset 1. The file at ```UT-EndoMRI/D1_MHS/D1-000/D1-000_ ut_r1.nii.gz``` is the uterus segmentation manually contoured by rater 1 for subject 000 in dataset 1. The file at```UT-EndoMRI/ D2_TCPW/D2-006/D2-006_ cy.nii.gz``` is the cyst segmentation manually contoured for subject 006 in dataset 2.

MRI sequences may be missing due to a lack of acquisition.

Train/Validation/Test Replication

The data split for RAovSeg training, validation, and testing is provided as follows:
- Training/validation subjects IDs: D2-000 – D2-007
- Testing subjects IDs: D2-008 – D2-037
All data in dataset 1, as well as other data in dataset 2, are not used in RAovSeg development.

Data Acquisition

This dataset was acquired at the Texas Medical Center, within the Memorial Hermann Hospital System and the Texas Children’s Hospital Pavilion for Women. The study and the data sharing were approved by the Committee for the Protection of Human Subjects at UTHealth (protocol no. HSC-SBMI-22-0184).

User Agreement

The UT-EndoMRI dataset is available for free use exclusively in non-commercial scientific research. Any publications resulting from its use must cite the following paper.

X. Liang, L.A. Alpuing Radilla, K. Khalaj, H. Dawoodally, C. Mokashi, X. Guan, K.E. Roberts, S.A. Sheth, V.S. Tammisetti, L. Giancardo. "A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis." (submitted)

Funding

This work has been supported by the Robert and Janice McNair Foundation.

Research Team

Here are the people behind this data acquisition effort:
Xiaomin Liang, Linda A Alpuing Radilla, Kamand Khalaj, Haaniya Dawoodally, Chinmay Mokashi, Xiaoming Guan, Kirk E Roberts, Sunil A Sheth, Varaha S Tammisetti, Luca Giancardo

Acknowledgements

We would also like to acknowledge for their support: Memorial Hermann Hospital System and Texas Children’s Hospital Pavilion for Women.

This dataset is about book subjects. It has 1 row and is filtered where the books is Women in leadership : contextual dynamics and boundaries. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The dataset contains x-ray images, mammography, from breast cancer screening at the Karolinska University Hospital, Stockholm, Sweden, collected by principal investigator Fredrik Strand at Karolinska Institutet. The purpose for compiling the dataset was to perform AI research to improve screening, diagnostics and prognostics of breast cancer.

The dataset is based on a selection of cases with and without a breast cancer diagnosis, taken from a more comprehensive source dataset.

1,103 cases of first-time breast cancer for women in the screening age range (40-74 years) during the included time period (November 2008 to December 2015) were included. Of these, a random selection of 873 cases have been included in the published dataset.

A random selection of 10,000 healthy controls during the same time period were included. Of these, a random selection of 7,850 cases have been included in the published dataset.

For each individual all screening mammograms, also repeated over time, were included; as well as the date of screening and the age. In addition, there are pixel-level annotations of the tumors created by a breast radiologist (small lesions such as micro-calcifications have been annotated as an area). Annotations were also drawn in mammograms prior to diagnosis; if these contain a single pixel it means no cancer was seen but the estimated location of the center of the future cancer was shown by a single pixel annotation.

In addition to images, the dataset also contains cancer data created at the Karolinska University Hospital and extracted through the Regional Cancer Center Stockholm-Gotland. This data contains information about the time of diagnosis and cancer characteristics including tumor size, histology and lymph node metastasis.

The precision of non-image data was decreased, through categorisation and jittering, to ensure that no single individual can be identified.

The following types of files are available: - CSV: The following data is included (if applicable): cancer/no cancer (meaning breast cancer during 2008 to 2015), age group at screening, days from image to diagnosis (if any), cancer histology, cancer size group, ipsilateral axillary lymph node metastasis. There is one csv file for the entire dataset, with one row per image. Any information about cancer diagnosis is repeated for all rows for an individual who was diagnosed (i.e., it is also included in rows before diagnosis). For each exam date there is the assessment by radiologist 1, radiologist 2 and the consensus decision. - DICOM: Mammograms. For each screening, four images for the standard views were acuqired: left and right, mediolateral oblique and craniocaudal. There should be four files per examination date. - PNG: Cancer annotations. For each DICOM image containing a visible tumor.

Access: The dataset is available upon request due to the size of the material. The image files in DICOM and PNG format comprises approximately 2.5 TB. Access to the CSV file including parametric data is possible via download as associated documentation.

This dataset contains 2 x .csv files (preservation copies), 1 x txt file (preservation copy, variable description) and 2 .sav (Original files in SPSS file format) files examining gender differences in spatial ability.The study examined whether women excel at tasks which require processing the identity of objects information as has been suggested in the context of the well-known object location memory task. In a computer-simulated task, university students were shown simulated indoor and outdoor house scenes. After studying a scene the students were presented with two images. One was the original image and the other a modified version in which one object was either rotated by ninety degrees or substituted with a similar looking object. The participants were asked to indicate the original image.The main finding was that no sex effect was obtained in this task. The female and male students did not differ on a verbal ability test, and their 2D:4D ratios were found to be comparable.

Total Quantity - Total quantity is the number of items multiplied by the quantity prescribed. e.g. 2 items prescribed, one with a quantity of 2 and one with a quantity of 3, the total quantity would show as 5 (1 item x quantity of 2) + (1 item x quantity of 3) Net Ingredient Cost (NIC(£)) - Net Ingredient cost (NIC) is the basic price of a drug as stated in Part II Clause 8 of the Drug Tariff but please note that where a price concession for items listed in Part VIII of the Drug Tariff has been agreed between the Department of Health and Social Care (DHSC) and the Pharmaceutical Services Negotiating Committee the NIC will reflect the concession price rather than the Drug Tariff price. Gender - Patient gender has been reported using the latest patient gender information held by the NHSBSA Information Services data warehouse at the time that the data was extracted. This uses information from either the most recent Electronic Prescription Service (EPS) message or from the last time that NHSBSA received data about the patient's gender from NHS Personal Demographics Service. Gender is displayed as Male, Female, Unknown or Unspecified. Unknown means not recorded. Unspecified means recorded but not as either Male or Female. This could mean male, female, transitioning or transitioned, or non-binary, just that the data is unclear intentionally or not Suppressions - Suppressions have been applied where items are lower than 5, for items and NIC and quantity for the following drugs and identified genders as per the sensitive drug list; • When the BNF Paragraph Code is 60401 (Female Sex Hormones and Their Modulators) and the gender identified on the prescription is Male • When the BNF Paragraph Code is 60402 (Male Sex Hormones and Antagonists) and the gender identified on the prescription is Female • When the BNF Paragraph Code is 70201 (Preparations for Vaginal/Vulval Changes) and the gender identified on the prescription is Male • When the BNF Paragraph Code is 70202 (Vaginal and Vulval Infections) and the gender identified on the prescription is Male • When the BNF Paragraph Code is 70301 (Combined Hormonal Contraceptives/Systems) and the gender identified on the prescription is Male • When the BNF Paragraph Code is 70302 (Progestogen-only Contraceptives) and the gender identified on the prescription is Male • When the BNF Paragraph Code is 80302 (Progestogens) and the gender identified on the prescription is Male • When the BNF Paragraph Code is 70405 (Drugs for Erectile Dysfunction) and the gender identified on the prescription is Female • When the BNF Paragraph Code is 70406 (Drugs for Premature Ejaculation) and the gender identified on the prescription is Female Please note that this request and our response is published on our Freedom of Information disclosure log at:

Additional file 3. Table S7 The association between male-specific aDMCs and X-chromosome gene expression in males (n=1337). Table S8 The association between female specific aVMCs and X-chromosome gene expression in females (n=1794).

X-linked multi-ancestry meta-analysis reveals tuberculosis susceptibility variants https://doi.org/10.5061/dryad.2z34tmpv5 For this X-chromosome specific sex-stratified meta-analysis multiple analysis were conducted. First, we did sex-stratified association analysis on all the individual datasets using the XWAS software and then the results were combined in multiple meta-analysis (also using XWAS). The results for the individual datasets are not available in this repository but can be requested through the corresponding authors in the published manuscript. The results for the meta-analysis are available in this repository. Multiple meta-analysis was conducted, a combined meta-analysis and a sex stratified meta-analysis, which were also further stratified by the source population subgroup. The following meta-analysis were conducted: 1. A combined meta-analysis using data across all populations for males and females. 2. A sex-stratified meta-analysis including data across all populations. 3. A combined meta-analysis for the Asian, Euroasian and African populations 4. Sex-stratified meta-analysis for the Asian, Euroasian and African populations. The results from this analysis identified novel genetic variants with strong sex-specific effects. While previous X-linked associations were not duplicated in this study the analysis revealed associations in genomic regions that overlap with previous studies. ## Description of the data and file structure Files included in this repository: 1. Plink_male_female_combined_meta_analysis_all_cohorts.meta a. Meta-analysis containing results from all datasets from males and females (not stratified by sex or ancestry). b. Produced using PLINK software 2. XWAS_female_ALL_cohorts_meta_analysis.meta a. Meta-analysis containing results from the females of all datasets. (not stratified by ancestry) b. Produced using the XWAS software 3. XWAS_male_ALL_cohorts_meta_analysis.meta a. Meta-analysis containing results from the males of all datasets. (not stratified by ancestry) b. Produced using the XWAS software 4. XWAS_female_chinese_cohorts_meta_analysis.meta a. Meta-analysis containing results from the females of all datasets of Asian ancestry. b. Produced using the XWAS software 5. XWAS_male_chinese_cohorts_meta_analysis.meta a. Meta-analysis containing results from the males of all datasets of Asian ancestry. b. Produced using the XWAS software 6. XWAS_female_euroasian_cohorts_meta_analysis.meta a. Meta-analysis containing results from the females of all datasets of Asian and European ancestry. b. Produced using the XWAS software 7. XWAS_male_euroasian_cohorts_meta_analysis.meta a. Meta-analysis containing results from the males of all datasets of Asian and European ancestry. b. Produced using the XWAS software 8. XWAS_female_african_cohorts_meta_analysis.meta a. Meta-analysis containing results from the females of all datasets of African ancestry. b. Produced using the XWAS software 9. XWAS_male_african_cohorts_meta_analysis.meta a. Meta-analysis containing results from the males of all datasets of African ancestry. b. Produced using the XWAS software The files contain the association testing results for all variants on the X chromosome. For the meta-analysis output files the column descriptions are as follows: CHR: Chromosome number ‘23’ representing the X chromosome BP: The base pair position of the genetic variants (build 37 locations) SNP: SNP names presented as ‘CHR:BP’ A1: Major allele A2: Minor allele N: Number of studies in the meta-analysis for each variant P: P-value of the association testing P(R): P-value of the residual OR: Odds ratio of the association testing for each variant OR(R): Odds ratio of the residual Q: Cochran’s Q measure of heterogeneity, which is calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies I: The I² statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance ## Sharing/Access information The meta-analysis results can be downloaded from this repository. The original raw data and summary statistics of the association testing of the individual files are not available due to ethical and data sharing constraints. These files can be requested through the corresponding authors listed in the published manuscript. Data was derived from the following sources: * International Tuberculosis Host Genetics Consortium ## Code/Software Imputation of the individual data was done using the Impute2 software. Quality control of the data was done using PLINK and XWAS software. Association testing of the individual files and subsequent meta-analysis were also performed using the PLINK and XWAS software. This analysis includes 7 of the 17 published (and unpublished) GWAS studies of TB (with HIV-negative cohorts) prior to 2022. It excludes data from Ice...

TwoWomenInWood1883_VanGogh_vs_TreeOil_18TorqueXraySet Overview This dataset explores the deep torque-based relationship between Two Women in the Wood (1883) by Vincent van Gogh and The Tree Oil Painting (undated). Using the 18 Supreme Techniques, X-ray overlays, and AI feature matching, the dataset provides high-resolution analysis of gesture, energy, and compositional force — revealing a structural similarity score of 96.1%.

Core Contents Original painting image of Two Women in the Wood Tree… See the full description on the dataset page: https://huggingface.co/datasets/HaruthaiAi/TwoWomenInWood1883_VanGogh_vs_TreeOil_18TorqueXraySet.

This dataset tracks the updates made on the dataset "Every Woman Counts Regional Contractors Map" as a repository for previous versions of the data and metadata.

This classification dataset is from Kaggle and was uploaded to Kaggle by Paul Mooney.

It contains over 5,000 images of chest x-rays in two categories: "PNEUMONIA" and "NORMAL."

• Version 1 contains the raw images, and only has the pre-processing feature of "Auto-Orient" applied to strip out EXIF data, and ensure all images are "right side up."
• Version 2 contains the raw images with pre-processing features of "Auto-Orient" and Resize of 640 by 640 applied
• Version 3 was trained with Roboflow's model architecture for classification datasets and contains the raw images with pre-processing features of "Auto-Orient" and Resize of 640 by 640 applied + augmentations:
  • Outputs per training example: 3
  • Shear: ±3° Horizontal, ±2° Vertical
  • Saturation: Between -5% and +5%
  • Brightness: Between -5% and +5%
  • Exposure: Between -5% and +5%

Below you will find the description provided on Kaggle:

Context

http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 https://i.imgur.com/jZqpV51.png" alt="Figure S6"> Figure S6. Illustrative Examples of Chest X-Rays in Patients with Pneumonia, Related to Figure 6 The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs. http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5

Content

The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).

Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.

For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.

Acknowledgements

Data: https://data.mendeley.com/datasets/rscbjbr9sj/2

License: CC BY 4.0

Citation: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 https://i.imgur.com/8AUJkin.png" alt="citation - latest version (Kaggle)">

Inspiration

Automated methods to detect and classify human diseases from medical images.