The dataset used in this study consists of 7,632 mammogram images categorized into two classes: 2,520 benign and 5,112 malignant images from Huang and Lin (2020). The mammography images in the INbreast database were originally collected from the Centro Hospitalar de S. Joao (CHSJ) Breast Center in Porto. The database contains data collected from August 2008 to July 2010 and includes 115 cases with a total of 410 images (Moreira et al., 2012). Of these, 90 cases concern women with abnormalities in both breasts. Four different types of breast disease are recorded in the database: Mass, calcification, asymmetries and distortions. The mammograms are recorded from two standard perspectives: Craniocaudal (CC) and Mediolateral Oblique (MLO). In addition, breast density is classified into four categories based on the BI-RADS standards: Fully Fat (Density 1), Scattered Fibrous-Landular Density (Density 2), Heterogeneously Dense (Density 3) and Extremely Dense (Density 4). The images are stored in two resolutions: 3328 x 4084 pixels or 2560 x 3328 pixels, in DICOM format. 106 mammograms depicting breast masses were selected from the INbreast database. To enhance the dataset for model training, data augmentation techniques were applied, increasing the total number of breast mammography images to 7,632.

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.

examined regional LNs

Generated using https://www.kaggle.com/code/theoviel/dicom-resized-png-jpg

• 256x256 : https://www.kaggle.com/datasets/theoviel/rsna-breast-cancer-256-pngs
• 512x512 : https://www.kaggle.com/datasets/theoviel/rsna-breast-cancer-512-pngs
• 1024x1204: https://www.kaggle.com/datasets/theoviel/rsna-breast-cancer-1024-pngs

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

This dataset originates from a deep learning model trained on the "Coimbra Breast Cancer" dataset, with feature distributions closely resembling the original. The original data includes clinical observations from 64 patients with breast cancer and 52 healthy controls, encompassing 10 quantitative predictors and a binary dependent variable indicating the presence or absence of breast cancer.

Quantitative Attributes:

Age (years): Represents the age of individuals in the dataset.

BMI (kg/m²): Body Mass Index, a measure of body fat based on weight and height.

Glucose (mg/dL): Reflects blood glucose levels, a vital metabolic indicator.

Insulin (µU/mL): Indicates insulin levels, a hormone associated with glucose regulation.

HOMA: Homeostatic Model Assessment, a method assessing insulin resistance and beta-cell function.

Leptin (ng/mL): Represents leptin levels, a hormone involved in appetite and energy balance regulation.

Adiponectin (µg/mL): Reflects adiponectin levels, a protein associated with metabolic regulation.

Resistin (ng/mL): Indicates resistin levels, a protein implicated in insulin resistance.

MCP-1 (pg/dL): Reflects Monocyte Chemoattractant Protein-1 levels, a cytokine involved in inflammation.

Labels:

1: Healthy controls

2: Patients with breast cancer

These quantitative attributes, including anthropometric data and parameters gathered from routine blood analysis, serve as the foundation for potential biomarkers of breast cancer. The dataset presents an opportunity for developing accurate prediction models, aiding in the identification and understanding of factors associated with breast cancer.

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.

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...

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.

The multispectral breast cancer image datasets span three complementary imaging modalities: Ultrasound, Histopathological, and Chest X-ray. Each dataset includes balanced classes of benign and malignant cases, and the images are enhanced through spectral conversion (RGB, HSV, Jet) to support robust multispectral analysis for classification and fusion tasks.

MSI Ultrasound Breast Images for Breast Cancer This dataset contains ultrasound images of breast tissue labeled as either benign or malignant.

Total Images: 806

Benign: 406 images

Malignant: 400 images

Augmentation: Data augmentation techniques such as rotation and sharpening were applied to enhance the diversity and volume of the dataset, enabling robust training of machine learning models.

MSI BreastHis – Breast Cancer Histopathological Images This dataset comprises high-resolution microscopic images of breast tumor tissue collected for histopathological analysis. These images provide cellular-level detail and are essential for determining cancer grade and type.

Total Images Used: 1,246 (Subset of the full BreakHis dataset)

Benign: 623 images

Malignant: 623 images

MSI Chest X-Ray for Breast Cancer This dataset consists of colorized chest X-ray images used for identifying breast cancer-related anomalies. While traditionally not the primary modality for breast cancer detection, chest X-rays can provide useful structural insights when used in conjunction with other imaging types.

Total Images: 1,000

Benign: 500 images

Malignant: 500 images

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 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.

BreastCancer YOLOv8

## Overview

BreastCancer YOLOv8 is a dataset for object detection tasks - it contains Breastcancer Yolov8 Cancer annotations for 1,642 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).

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)

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.

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.

categorizing

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