Breast Cancer Wisconsin Diagnostic Dataset

Following description was retrieved from breast cancer dataset on UCI machine learning repository. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at here. Separating plane described above was obtained using Multisurface Method-Tree (MSM-T), a classification method which uses linear… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin.

Description

Images of histological sections with and without breast cancer, using the Biglycan biomarker.Jan 26, 2023

Categories

Biomarkers, Breast Cancer

Acknowledgements & Source

Pedro Neto

Institutions

Universidade do Vale do Rio dos Sinos, Hospital de Clinicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Instituto Federal de Educacao Ciencia e Tecnologia de Mato Grosso

Data Source

Image source: Vitro Vivo Biotech

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Dataset Card for "breast-cancer"

Dataset was taken from the MedSAM project and used in this notebook which fine-tunes Meta's SAM model on the dataset. More Information needed

categorizing

This is a dataset about breast cancer occurrences.

This dataset is taken from OpenML - breast-cancer

This breast cancer domain was obtained from the University Medical Centre, Institute of Oncolog...

Source:

Copied from the original dataset

  Creators:

Dr. William H. Wolberg, General Surgery Dept. University of Wisconsin, Clinical Sciences Center Madison, WI 53792 wolberg '@' eagle.surgery.wisc.edu

W. Nick Street, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 street '@' cs.wisc.edu 608-262-6619

Olvi L. Mangasarian, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 olvi '@' cs.wisc.edu… See the full description on the dataset page: https://huggingface.co/datasets/wwydmanski/wisconsin-breast-cancer.

Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.

Here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases. The dataset contains four components: (1) DICOM images, (2) a spreadsheet indicating which group each case belongs to (3) annotation boxes, and (4) Image paths for patients/studies/views. A detailed description of this dataset can be found in the following paper; please reference this paper if you use this dataset:

M. Buda, A. Saha, R. Walsh, S. Ghate, N. Li, A. Święcicki, J. Y. Lo, M. A. Mazurowski, Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model. (https://doi.org/10.1001/jamanetworkopen.2021.19100).

Additional information and resources related to this dataset can be found here: https://sites.duke.edu/mazurowski/resources/digital-breast-tomosynthesis-database/

A Version 1 of the dataset contains only a subset of all data described in the paper above. More data will be share in subsequent versions.

Please visit this discussion forum for any questions related to the data: https://www.reddit.com/r/DukeDBTData/

Required Preprocessing of DBT Images

For some of the images, the laterality stored in the DICOM header and/or image orientation are incorrect. The reference standard "truth" boxes are defined with respect to the corrected image orientation in these instances. Therefore, it is crucial to provide your results for images in the correct image orientation. Python functions for loading image data from a DICOM file into 3D array of pixel values in the correct orientation and for displaying "truth" boxes (if any) are on GitHub. Please see the readme file there for instructions.

DBTex Lesion Detection Challenge Predictions

The DBTex lesion detection challenge tasked participating teams with detecting lesions in the BCS-DBT test set. The challenge had two phases: DBTex1 and DBTex2. Here we provide the BCS-DBT lesion predictions made by all participating teams for both phases, for both the BCS-DBT test and validation sets, as “team_predictions_bothphases.zip”. Please see here under “Output format for the DBTex2 Challenge test set results” for a description of how these results are formatted. Finally, when comparing lesion bounding box predictions to the image data, be sure to load the images correctly according to the above “Required Preprocessing of DBT Images”.

If you use these predictions, please reference the DBTex challenge paper:

Konz N, Buda M, Gu H, et al. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. JAMA Netw Open. 2023;6(2):e230524. doi:10.1001/jamanetworkopen.2023.0524

The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). It contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format). This database has been built in collaboration with the P&D Laboratory - Pathological Anatomy and Cytopathology, Parana, Brazil.

Paper: F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "A Dataset for Breast Cancer Histopathological Image Classification," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455-1462, July 2016, doi: 10.1109/TBME.2015.2496264

According to the WHO, breast cancer is the most commonly occurring cancer worldwide. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet breast cancer mortality in high-income countries has dropped by 40% since the 1980s when health authorities implemented regular mammography screening in age groups considered at risk. Early detection and treatment are critical to reducing cancer fatalities, and your machine learning skills could help streamline the process radiologists use to evaluate screening mammograms. Currently, early detection of breast cancer requires the expertise of highly-trained human observers, making screening mammography programs expensive to conduct. RSNA collected screening mammograms and supporting information from two sites, totaling just under 20,000 imaging studies.

examined regional LNs

This dataset consists of 1 .xlsx file, 2 .png files, 1 .json file and 1 .zip file:annotation_details.xlsx: The distribution of annotations in the previously mentioned six classes (mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule) is presented in a Excel spreadsheet.original.png: The input image.annotated.png: An example from the dataset. In the annotated image, blue circles indicate the tumor nuclei, pink circles show non-tumor nuclei such as blood cells, stroma nuclei, and lymphocytes; orange and green circles are mitosis and apoptosis, respectively; light blue circles are true lumen for tubules, and yellow circles represent white regions (non-lumen) such as fat, blood vessel, and broken tissues.data.json: The annotations for the BreCaHAD dataset are provided in JSON (JavaScript Object Notation) format. In the given example, the JSON file (ground truth) contains two mitosis and only one tumor nuclei annotations. Here, x and y are the coordinates of the centroid of the annotated object, and the values are between 0, 1.BreCaHAD.zip: An archive file containing dataset. Three folders are included: images (original images), groundTruth (json files), and groundTruth_display (groundTruth applied on original images)

This dataset contains images of mammograms and can be used for research and education purposes only. The dataset contains DCM images, TIFF images, a Radiology report, a Segmented mask, and pixel level annotation on abnormal regions and csv file that contains other metadata.

Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.

Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. This dataset provides histopathology images and paired CK5/6 immunohistochemical staining images from patients with DCISM, as well as multiphoton microscopy images of suspicious regions. It offers multi-modal imaging data from various perspectives for analysis and diagnosis of microinvasive breast cancer by other researchers in the field.

The dataset contains data from 12 breast cancer patients, including 10 cases of ductal carcinoma in situ with microinvasion (DCISM), 1 case of ductal carcinoma in situ (DCIS), and 1 case of invasive breast cancer.

The magnification of the glass slide images is 40x. The pathology slide scanner used was created by the Sunny Optical Technology (group) Co., Ltd., and the pixel aspect ratio of the images is 1. The dataset also includes multiphoton microscopy imaging of suspicious microinvasion areas. The multiphoton imaging system was manufactured by Zeiss, and it also has a pixel aspect ratio of 1.

Our database was specifically collected for the use of imaging methods in diagnosing DICSM. The suffixes in each case number indicate the patient's condition - "DCISM" for ductal carcinoma in situ with microinvasion, "DCIS" for ductal carcinoma in situ, and "IDC" for invasive ductal carcinoma. Apart from these labels, we have not collected any additional clinical information for these cases.

The global breast cancer screening tests market was valued at US$ 1.9 Billion in 2022 and is expected to reach US$ 4.4 Billion by 2033. The imaging tests segment with around 54.3% value share, has topped the global market within the product category in 2022 and is expected to grow at a CAGR of close to 7.8% over the forecast period (2023 to 2033).

Data Points	Market Insights
Market Value 2022	US$ 1.9 Billion
Market Value 2023	US$ 2.1 Billion
Market Value 2033	US$ 4.4 Billion
CAGR 2023 to 2033	7.8%
Market Share of Top 5 Countries	54.5%
Key Market Players	AstraZeneca, Novartis, Sanofi, Pfizer, Bayer AG, GlaxoSmithKline plc, and Siemens Healthineers, Hologic Inc.

Report Scope as Per Breast Cancer Screening Test Industry Analysis

Attribute	Details
Forecast Period	2023 to 2033
Historical Data Available for	2017 to 2022
Market Analysis	US$ Million for Value
Key Regions Covered	North America, Latin America, Europe, South Asia, East Asia, Oceania, Middle East and Africa (MEA)
Key Countries Covered	USA, Canada, Brazil, Mexico, Argentina, Germany, Italy, France, UK, Spain, BENELUX, Russia, China, Japan, South Korea, India, Indonesia, Thailand, Philippines, Malaysia, Australia, New Zealand, GCC countries, Türkiye, Northern Africa and South Africa.
Key Market Segments Covered	Diagnostic Test Type, End User, and Region
Key Companies Profiled	AstraZeneca Novartis Sanofi Pfizer Bayer AG GlaxoSmithKline plc Siemens Healthineers Hologic Inc. General Electric Company Koninklijke Philips NV Fujifilm Holdings A&G Pharmaceutical, Inc. Biocrates Life Sciences AG Metabolomic Technologies, Inc. Myriad Genetics OncoCyte Corporation POC Medical Systems, Inc. Hologic, Inc. Provista Diagnostics, Inc. BioTime, Inc.
Report Coverage	Market Forecast, Competition Intelligence, DROT Analysis, Market Dynamics and Challenges, Strategic Growth Initiatives
Pricing	Available upon Request

Breasts Cancer

## Overview

Breasts Cancer is a dataset for object detection tasks - it contains Mammography Breast Cancer Detec annotations for 638 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Breast cancer is a major cause of morbidity and mortality for women in Sub-Saharan Africa and for black American women. There is evidence that the pathologic characteristics of breast cancers in both African women and black American women may differ from their counterparts of European ancestry. However, despite the great burden of disease, data on pathologic features of breast carcinoma in Sub-Saharan Africa is limited and often contradictory. This lack of information makes it difficult to prioritize resource use in efforts to improve breast cancer outcomes in the region. We examined consecutive cases of breast cancer in Tanzanian women (n=83), black American women (n=120), and white American women (n=120). Each case was assessed for tumor type, grade, mitotic count, ER and HER2 status, and tumor infiltrating lymphocyte involvement. The Tanzanian subjects were younger and had higher stage tumors than the subjects in either American group. Breast cancers in the Tanzanian and black American groups were more likely to be high grade (p=0.008), to have a high mitotic rate (p<0.0001), and to be ER-negative (p<0.001) than the tumors in the white American group. Higher levels of tumor infiltrating lymphocyte involvement were seen among Tanzanian and black American subjects compared to white American subjects (p=0.0001). Among all subjects, tumor infiltrating lymphocyte levels were higher in tumors with a high mitotic rate. Among Tanzanian and black American subjects, tumor infiltrating lymphocyte levels were higher in ER-negative tumors. These findings have implications for treatment priorities for breast cancer in Tanzania and other Sub-Saharan African countries. ... [Read More]

This dataset consists of 361 whole slide images (WSI) - 296 malignant from women with invasive breast cancer (HER2 neg) and 65 benign. The tumours have been classified with four SNOMED-CT categories based on morphology: invasive duct carcinoma, invasive lobular carcinoma, in situ carcinoma, and others. 4144 separate annotations have been made to segment different tissue structures connected to ontologies.

BIT/breast-cancer-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community