This dataset of breast cancer patients was obtained from the 2017 November update of the SEER Program of the NCI, which provides information on population-based cancer statistics. The dataset involved female patients with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3) diagnosed in 2006-2010. Patients with unknown tumour size, examined regional LNs, positive regional LNs, and patients whose survival months were less than 1 month were excluded; thus, 4024 patients were ultimately included.

1) Data Introduction • The Breast Cancer Wisconsin (Diagnostic) data focuses on distinguishing between malignant (cancerous) and benign (non-cancerous) breast tumors. This dataset is crucial for developing machine learning models to aid in the early detection and classification of breast cancer, thereby potentially saving lives through timely intervention.

2) Data Utilization (1) Breast cancer data has characteristics that: • The dataset contains various features extracted from digitized images of fine needle aspirate (FNA) of breast masses, allowing for detailed analysis and classification of tumors. (2) Breast cancer data can be used to: • Healthcare and Medical Research: Useful for developing diagnostic tools and models to accurately classify breast tumors, aiding healthcare providers in making informed decisions. • Machine Learning and AI Development: Assists in creating and fine-tuning machine learning algorithms to improve predictive accuracy in medical diagnostics.

Dataset

This dataset was created by Figo

Released under MIT

Contents

Context

The resources for this dataset can be found at https://www.openml.org/d/13 and https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

Content

This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.

Number of Instances: 286

Number of Attributes: 9 + the class attribute

Attribute Information:

Class: no-recurrence-events, recurrence-events age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99. menopause: lt40, ge40, premeno. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26, 27-29, 30-32, 33-35, 36-39. node-caps: yes, no. deg-malig: 1, 2, 3. breast: left, right. breast-quad: left-up, left-low, right-up, right-low, central. irradiat: yes, no.

Missing Attribute Values: (denoted by “?”) Attribute #: Number of instances with missing values: 6. 8 9. 1.

Class Distribution:

no-recurrence-events: 201 instances recurrence-events: 85 instances

Acknowledgements

Original data https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

Inspiration

With the attributes described above, can you predict if a patient has recurrence event ?

This dataset is taken from the UCI Machine Learning Repository (Link: https://data.world/health/breast-cancer-wisconsin) by the Donor: Nick Street

The main idea and inspiration behind the upload was to provide datasets for Machine Learning as practice and reference for my peers at college. The main purpose is to analyze data and experiment with different machine learning ideas and techniques for this binary classification task. As such, this dataset is a very useful resource to practice on.

Breast cancer is when breast cells mutate and become cancerous cells that multiply and form tumors. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. Breast cancer typically affects women and people assigned female at birth (AFAB) age 50 and older, but it can also affect men and people assigned male at birth (AMAB), as well as younger women. Healthcare providers may treat breast cancer with surgery to remove tumors or treatment to kill cancerous cells.

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 http://www.cs.wisc.edu/~street/images/

The task: To classify whether the tumor is benign (B) or malignant (M).

Relevant information

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
http://www.cs.wisc.edu/~street/images/

Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree. Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.

The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].


This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

Number of instances: 569

Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)

Original 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 

Donor: Nick Street

Date: November 1995

Past Usage:

first usage:

W.N. Street, W.H. Wolberg and O.L. Mangasarian 
Nuclear feature extraction for breast tumor diagnosis.
IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science
and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.

OR literature:

O.L. Mangasarian, W.N. Street and W.H. Wolberg. 
Breast cancer diagnosis and prognosis via linear programming. 
Operations Research, 43(4), pages 570-577, July-August 1995.

Medical literature:

W.H. Wolberg, W.N. Street, and O.L. Mangasarian. 
Machine learning techniques to diagnose breast cancer from
fine-needle aspirates. 
Cancer Letters 77 (1994) 163-171.

W.H. Wolberg, W.N. Street, and O.L. Mangasarian. 
Image analysis and machine learning applied to breast cancer
diagnosis and prognosis. 
Analytical and Quantitative Cytology and Histology, Vol. 17
No. 2, pages 77-87, April 1995. 

W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. 
Computerized breast cancer diagnosis and prognosis from fine
needle aspirates. 
Archives of Surgery 1995;130:511-516.

W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. 
Computer-derived nuclear features distinguish malignant from
benign breast cytology. 
Human Pathology, 26:792--796, 1995.

See also: http://www.cs.wisc.edu/~olvi/uwmp/mpml.html http://www.cs.wisc.edu/~olvi/uwmp/cancer.html

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

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.

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

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.

examined regional LNs

The AISSLab Breast Cancer Dataset is a collection of mammogram images by experts from the Ma'amon's Diagnostic Centre Mammogram Images for Breast Cancer (MDCMI-BC) in Yemen. It is designed to support advancements in breast cancer research and computer-aided diagnosis (CAD) systems. To facilitate research in breast cancer detection, focusing on harmonizing AI with diverse imaging data. This dataset emphasizes improving diagnostic accuracy and is available for academic and clinical research applications.

If you are using this dataset for research purpose kindly cite the following papers:

[1] A. M. Al-Hejri, R. M. Al-Tam, M. Fazea, A. H. Sable, S. Lee, and M. A. Al-antari, “ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images,” Diagnostics, vol. 13, no. 1, p. 89, Dec. 2022, doi: 10.3390/diagnostics13010089.

[2] R. M. Al-Tam, A. M. Al-Hejri, S. S. Alshamrani, M. A. Al-antari, and S. M. Narangale, “Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images,” Biocybern. Biomed. Eng., vol. 44, no. 3, pp. 731–758, Jul. 2024, doi: 10.1016/j.bbe.2024.08.007.

Pathological Section of Breast Cancer Cells

Breast cancer dataset cited in 'Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters'

PICO breast cancer dataset

This dataset has been extracted from PICO-Corpus. The corpus consists of 1,011 abstracts of breast cancer randomized controlled trials extracted from PubMed. The PICO breast cancer dataset contains a total of 26 entities, compared to the usual 4 found in PICO corpora. Specifically, the following image extracted by the dataset's authors shows the hierarchy of the entities. The preprocessed dataset, ready to serve as inputs for MLMs such as BERT-like… See the full description on the dataset page: https://huggingface.co/datasets/cuevascarlos/PICO-breast-cancer.

This dataset was created to be used in machine learning tasks for both binary and multiclass classification problems. It consists of 7,909 microscopic images of breast tumor tissue, captured using different magnification factors (40X, 100X, 200X, and 400X). The images in this dataset were resized to 224x224 pixels and organized according to binary and multiclass classification tasks.

THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

Interdisciplinary s... Identifier DOI https://doi.org/10.17632/jxwvdwhpc2.1 PID https://nbn-resolving.org/urn:nbn:nl:ui:13-i2-oi2g Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:301675 Provenance Creator Pereira, M Publisher Data Archiving and Networked Services (DANS) Contributor Mayke Pereira Publication Year 2023 Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/licenses/by/4.0; http://creativecommons.org/licenses/by/4.0 OpenAccess true Representation Resource Type Dataset Discipline Other

Pereira, Mayke (2023), “BreakHis - Breast Cancer Histopathological Database”, Mendeley Data, V1, doi: 10.17632/jxwvdwhpc2.1

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.

Breast Cancer Dataset

categorizing

Breast cancer is the leading cause of cancer death among women worldwide. The vast majority of breast cancers are carcinomas that originate from cells lining the milk-forming ducts of the mammary gland. The molecular subtypes of breast cancer, which are based on the presence or absence of hormone receptors (estrogen and progesterone subtypes) and human epidermal growth factor receptor-2 (HER2), include: * Luminal A subtype: Hormone receptor positive (progesterone and estrogen) and HER2 (ERBB2) negative * Luminal B subtype: Hormone receptor positive (progesterone and estrogen) and HER2 (ERBB2) positive * HER2 positive: Hormone receptor negative (progesterone and estrogen) and HER2 (ERBB2) positive * Basal-like or triple-negative (TNBCs): Hormone receptor negative (progesterone and estrogen) and HER2 (ERBB2) negative Hormone receptor positive breast cancers are largely driven by the estrogen/ER pathway. In HER2 positive breast tumors, HER2 activates the PI3K/AKT and the RAS/RAF/MAPK pathways, and stimulate cell growth, survival and differentiation. In patients suffering from TNBC, the deregulation of various signaling pathways (Notch and Wnt/beta-catenin), EGFR protein have been confirmed. In the case of breast cancer only 8% of all cancers are hereditary, a phenomenon linked to genetic changes in BRCA1 or BRCA2. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers. Phosphorylation sites were added based on information from PhosphoSitePlus (R), www.phosphosite.org.