Rationale and objectives: Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database. Materials and methods: Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital s Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used. Results: The new database-INbreast-h

Inbreast

## Overview

Inbreast is a dataset for object detection tasks - it contains Breast 3OpO W7Rm annotations for 343 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 [ODbL v1.0 license](https://creativecommons.org/licenses/ODbL v1.0).

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.

Inbreast Class

## Overview

Inbreast Class is a dataset for object detection tasks - it contains Tumor annotations for 694 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).

The mammography dataset includes both benign and malignant tumors. In order to create the pictures for this dataset, 106 masses from the INbreast dataset, 53 masses from the MIAS dataset, and 2188 masses from the DDSM dataset were initially extracted. Then, we preprocess our photos using contrast-limited adaptive histogram equalization and data augmentation. Inbreast dataset has 7632 photos, MIAS dataset has 3816 images, and DDSM dataset includes 13128 images after data augmentation. Additionally, we combine DDSM, MIAS, and INbreast. The size of each image was changed to 227*227 pixels.

INBreast

## Overview

INBreast is a dataset for object detection tasks - it contains Breast Mass annotations for 641 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

The dataset used for the experiments of the paper "CORE-PERIPHERY PRINCIPLE GUIDED REDISIGN OF SELF-ATTENTION IN TRANSFORMERS"

InBreast

## Overview

InBreast is a dataset for instance segmentation tasks - it contains Mass annotations for 458 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).

The INbreast dataset results applied using the VGG-16 pre-trained CNN, and GWO.

This dataset provides manually created segmentation masks of the images in the INbreast dataset by I.C. Moreira et al[1]. The masks are saved as nrrd files with pixel-wise ground truth for background (0), breast (1), and pectoral muscle (2) (when present). This dataset is created for the development of a mammogram segmentation model[2].

Segmentation masks were created in three steps, first initialization of the breast boundary by Otsu thresholding[3], second a pectoral muscle initialization for MLO images, and lastly a manual adjustment of the mask, as show in Figure 1. For the MLO views, the already publicly-available annotations of the pectoral muscle were used as the initialization. Finally, each segmentation mask was checked visually and adjusted manually using ITK-SNAP 3.6.0[4] by one of four medical imaging scientists with experience in mammography. This also includes adding pectoral muscle annotation were it was visible in CC views.

[1] I. C. Moreira et al., "INbreast: Toward a Full-field Digital Mammographic Database", Acad. Radiol. 19(2), 236–248 (2012)[2] S.D. Verboom et al., "Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors", Journal of Medical Imaging, 11(1), 014001 (2023)[3] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms", IEEE Trans. Syst. Man. Cybern. 9(1), 62–66 (1979)[4] P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability", Neuroimage 31(3), 1116–1128 (2006)

PurposeUsing computer-aided design (CAD) systems, this research endeavors to enhance breast cancer segmentation by addressing data insufficiency and data complexity during model training. As perceived by computer vision models, the inherent symmetry and complexity of mammography images make segmentation difficult. The objective is to optimize the precision and effectiveness of medical imaging.MethodsThe study introduces a hybrid strategy combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA), resulting in improved computational efficiency and performance. The implementation of Shape-guided segmentation (SGS) during the initialization phase, coupled with the elimination of convolutional layers, enables the model to effectively reduce computation time. The research proposes a novel loss function that combines segmentation losses from both components for effective training.ResultsThe robust technique provided aims to improve the accuracy and consistency of breast tumor segmentation, leading to significant improvements in medical imaging and breast cancer detection and treatment.ConclusionThis study enhances breast cancer segmentation in medical imaging using CAD systems. Combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA) is a hybrid approach that improves performance and computational efficiency by dealing with complex data and not having enough training data. The approach also reduces computing time and improves training efficiency. The study aims to improve breast cancer detection and treatment methods in medical imaging technology.

Dataset

This dataset was created by Rehel_Zannat

Contents

INbreast BD

## Overview

INbreast BD is a dataset for classification tasks - it contains BD Level annotations for 410 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).

The dataset contains mammography with benign and malignant masses. Images in this dataset were first extracted 106 masses images from INbreast dataset, 53 masses images from MIAS dataset, and 2188 masses images DDSM dataset. Then we use data augmentation and contrast-limited adaptive histogram equalization to preprocess our images. After data augmentation, Inbreast dataset has 7632 images, MIAS dataset has 3816 images, DDSM dataset has 13128 images. In addition, we also integrate INbreast, MIAS, DDSM together. All the images were resized to 227*227 pixels.

Dataset http://medicalresearch.inescporto.pt/breastresearch/index.php/Get_INbreast_Database

Dataset of Breast mammography images with Masses Published: 1 July 2020 DOI: 10.17632/ywsbh3ndr8.2 Contributors: Ting-Yu Lin, Mei-Ling Huang

All code used in this study is available at: https://github.com/hwejin23/MAMMO_Retinanet. (ZIP)

This CBIS-DDSM (Curated Breast Imaging Subset of DDSM) is an updated and standardized version of the Digital Database for Screening Mammography (DDSM). The DDSM is a database of 2,620 scanned film mammography studies. It contains normal, benign, and malignant cases with verified pathology information. The scale of the database along with ground truth validation makes the DDSM a useful tool in the development and testing of decision support systems. The CBIS-DDSM collection includes a subset of the DDSM data selected and curated by a trained mammographer. The images have been decompressed and converted to DICOM format. Updated ROI segmentation and bounding boxes, and pathologic diagnosis for training data are also included. A manuscript describing how to use this dataset in detail is available at https://www.nature.com/articles/sdata2017177.

Published research results from work in developing decision support systems in mammography are difficult to replicate due to the lack of a standard evaluation data set; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. Few well-curated public datasets have been provided for the mammography community. These include the DDSM, the Mammographic Imaging Analysis Society (MIAS) database, and the Image Retrieval in Medical Applications (IRMA) project. Although these public data sets are useful, they are limited in terms of data set size and accessibility.

For example, most researchers using the DDSM do not leverage all its images for a variety of historical reasons. When the database was released in 1997, computational resources to process hundreds or thousands of images were not widely available. Additionally, the DDSM images are saved in non-standard compression files that require the use of decompression code that has not been updated or maintained for modern computers. Finally, the ROI annotations for the abnormalities in the DDSM were provided to indicate a general position of lesions, but not a precise segmentation for them. Therefore, many researchers must implement segmentation algorithms for accurate feature extraction. This causes an inability to directly compare the performance of methods or to replicate prior results. The CBIS-DDSM collection addresses that challenge by publicly releasing an curated and standardized version of the DDSM for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography.

Please note that the image data for this collection is structured such that each participant has multiple patient IDs. For example, participant 00038 has 10 separate patient IDs which provide information about the scans within the IDs (e.g. Calc-Test_P_00038_LEFT_CC, Calc-Test_P_00038_RIGHT_CC_1). This makes it appear as though there are 6,671 patients according to the DICOM metadata, but there are only 1,566 actual participants in the cohort.

For scientific and other inquiries about this dataset, please contact TCIA's Helpdesk.

Whole slide imaging of 396 full cases of axillary lymph nodes in breast cancer cases. Included are both sentinel node surgery and axillary dissections pre, peri or post breast cancer surgery or treatment. Sentinel node cases are cut in three levels (stained with HE) and one additional slide immunohistochemically stained with CKAE1/AE3. The number of sentinel node cases with complete immunohistochemical staining is 321. The axillary dissections are cut with one cut level as default. No frozen sections included. The cases are anonymised and exported from the digital archive at the Department of Clinical Pathology in Linköping, Region Östergötland. Included are both positive and negative cases. Some metadata on case level is available (positive or negative case, number of nodes, primary tumour and if neoadjuvant treatment in axillary dissections was given).

Dataset

This dataset was created by FYP Group 10

Released under Apache 2.0

Contents

This blog post was posted by Sandeep Patel on June 18, 2015

Study Description from dbGaP:"Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) was one of five projects funded in 2010 as part of the NCI's Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative (http://epi.grants.cancer.gov/gameon/). GAME-ON's overall goal was to foster an intra-disciplinary and collaborative approach to the translation of promising research leads deriving from the initial wave of cancer GWAS. Specific goals included replication of previous GWAS findings and identification of new susceptibility loci through meta analyses of existing GWAS data and fine mapping of identified loci to better pinpoint causal variants; and identify germline variants that are associated with risk of multiple cancers.

To identify additional cancer risk loci, improve the precision of fine-mapping, and facilitate cross-cancer analyses, the GAME-ON projects and other consortia formed the OncoArray network, which developed and genotyped a new custom genotyping array (the "OncoArray") in large numbers of cancer cases and controls (over 400,000 samples). The OncoArray is a custom array manufactured by Illumina. The array includes a backbone of approximately 260,000 SNPs that provide genome-wide coverage of most common variants, together with markers of interest for each of the five GAME-ON cancers identified through genome-wide association studies (GWAS), fine-mapping of known susceptibility regions, sequencing studies, and other approaches. The array also includes loci of interest identified through studies of other cancer types, and other loci of interest to multiple cancer types (including loci associated with cancer related phenotypes, drug metabolism and radiation response). Additionally, SNPs relating to quantitative phenotypes such as body mass index (BMI), height, and breast density that correlate with common cancer risks are also included.

The DRIVE data included under this dbGAP submission include OncoArray data from 60,015 breast cancer cases and controls genotyped at the Center for Inherited Disease Research (CIDR), University of Cambridge, National Cancer Institute, University of Copenhagen, University of Southern California and Mayo Clinic."

Study Inclusion/Exclusion Criteria: " This project includes OncoArray data from 60,231 breast cancer cases and controls that were genotyped at the Center for Inherited Disease Research. These subjects were drawn from seventeen studies and were not excluded based on any of the following criteria: genotyping data call rate < 90%; genotyping data discordant from same sample's previous data (where available); duplicates within a study; male or gender unclear (XO, XXY); extreme heterozygosity in genotype data; phenotype and/or genotype data not consented for sharing via dbGAP."