322 x-ray images have been converted to png files from Mias Mammography dataset (Original: https://www.kaggle.com/datasets/kmader/mias-mammography)

The difference from the original: - PNG file - Image processed (CLAHE, Crop image,...)

Description

The BI-RADS classification was determined based on the values from the CLASS and SEVERITY columns. The CLASS column categorizes various types of abnormalities present in the mammogram images, such as CALC (Calcification), CIRC (Circumscribed masses), SPIC (Spiculated masses), and others, which form the basis for grouping the findings into relevant categories.

The character of the background tissue is represented by the DENSITY classification, which includes:

- F: Fatty
- G: Fatty-glandular
- D: Dense-glandular

These density categories provide crucial context for interpreting mammographic findings, as they can influence the visibility of abnormalities and the overall assessment of breast tissue health.

The CLASS column serves to group findings into categories such as:

- Normal: Indicates no abnormalities present.
- Masses: Refers to various types of masses detected, such as well-defined, spiculated, or ill-defined masses.
- Architectural Distortion & Asymmetry: Includes conditions that affect the overall structure of breast tissue without a clearly defined mass.
- Calcification: Involves the presence of calcium deposits that can indicate benign or malignant conditions.

This classification system allows for a standardized approach to evaluating mammograms, facilitating consistent communication and management of findings in clinical practice.

Acknowledgements/LICENCE

MAMMOGRAPHIC IMAGE ANALYSIS SOCIETY MiniMammographic Database LICENCE AGREEMENT This is a legal agreement between you, the end user and the Mammographic Image Analysis Society ("MIAS"). Upon installing the MiniMammographic database (the "DATABASE") on your system you are agreeing to be bound by the terms of this Agreement. GRANT OF LICENCE MIAS grants you the right to use the DATABASE, for research purposes ONLY. For this purpose, you may edit, format, or otherwise modify the DATABASE provided that the unmodified portions of the DATABASE included in a modified work shall remain subject to the terms of this Agreement. COPYRIGHT The DATABASE is owned by MIAS and is protected by United Kingdom copyright laws, international treaty provisions and all other applicable national laws. Therefore you must treat the DATABASE like any other copyrighted material. If the DATABASE is used in any publications then reference must be made to the DATABASE within that publication. OTHER RESTRICTIONS You may not rent, lease or sell the DATABASE. LIABILITY To the maximum extent permitted by applicable law, MIAS shall not be liable for damages, other than death or personal injury, whatsoever (including without limitation, damages for negligence, loss of business, profits, business interruption, loss of business information, or other pecuniary loss) arising out of the use of or inability to use this DATABASE, even if MIAS has been advised of the possibility of such damages. In any case, MIAS's entire liability under this Agreement shall be limited to the amount actually paid by you or your assignor, as the case may be, for the DATABASE.

Credits

Reference: J Suckling et al (1994): The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp375-378.

Mini MIAS

## Overview

Mini MIAS is a dataset for object detection tasks - it contains Categories annotations for 322 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).

A mammographic image analysis society (mias) database v1.21

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.

Mini MIAS (YOLO)

## Overview

Mini MIAS (YOLO) is a dataset for object detection tasks - it contains Mammograms 0gHy annotations for 322 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).

MIAS BD Classification

## Overview

MIAS BD Classification is a dataset for classification tasks - it contains Breast Density Level annotations for 322 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).

ZYXue/MIAS dataset hosted on Hugging Face and contributed by the HF Datasets community

This is a mammogram dataset downloaded fromhttp://peipa.essex.ac.uk/info/mias.htmlThis dataset is downloaded to perform a study on it.

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.

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

Malign and benign mammograms

Malignant and benign mammograms from INbreast, MIAS, and DDSM datasets, were downloaded directly from Lin, Ting-Yu, and Huang, Mei-Ling. Dataset of Breast mammography images with Masses https://doi.org/10.17632/ywsbh3ndr8.2

Normal mammograms

Normal mammograms were sourced from the DDSM webpage: http://www.eng.usf.edu/cvprg/Mammography/Database.html. However, the FTP service is currently not operational. Consequently, using BeautifulSoup (bs4) and PIL, thumbnails of all the normal datasets were extracted, resulting in a total of 2026 files. These files were then augmented and enhanced using CLAHE (Contrast Limited Adaptive Histogram Equalization).

Consult Jupyter Notebook for more information on the methods used for extraction and enhancing from webpage of DDSM

Experience the attraction 'Mia’s Elf Flight' anew! Perfectly plan with statistics from September 2025 on waiting times or queue times and weather data.

Dataset

This dataset was created by Ananthan123

Contents

MIA contributors to the customer service annual report.

(|λ1| < |λ2|).

The vibroacoustic characteristics of structures are vital in determining the operational envelope and mission feasibilities. The sources of vibroacoustic excitation are mainly due to noise generated by the launcher during ignition, lift-off, and atmospheric flight. Typically, foam or fiberglass claddings and cores or acoustic liners which incorporate resonating chambers are used to prevent the transmission of sound through such structural locations. However, this approach is found to be ineffective for vibroacoustic sources with dominant frequency content below 400 Hz. It is proposed to develop a metamaterial-inspired composite structure incorporating low-frequency vibro-impact resonating elements coupled with conventional high-frequency acoustic absorbers. The idea is to employ structurally-integral tuned resonators to pick up energy from incident low-frequency sound waves and utilizing the mechanism of frequency up-conversion via impacts, transfer the energy to higher modes in the sandwich primary structure for subsequent dissipation with conventional acoustic absorbers. The advantage of the proposed structure would be in reducing the transmitted pressure of low frequency waves, for which conventional methods are ineffective. Our initial bound for the attachment mass is within 5 to 10% of the baseline structure to show significant peak pressure reduction for LF waves. The state-of-the-art conventional absorbers provide about 10-20% sound transmission loss (STL) in the 100-150 Hz range. Our performance objective is to achieve STL of about 50-60% in frequency range below 400 Hz with 5-10% mass increase without deteriorating stiffness response of the structure. Successful completion of Phase I work will result in a "proof-of-concept" MIAS unit cell. In Phase II, detailed design and fabrication of the MIAS prototype panel will be completed.

Tab-MIA: A Benchmark for Membership Inference Attacks on Tabular Data

Tab-MIA is a benchmark dataset designed to evaluate the privacy risks of fine-tuning large language models (LLMs) on structured tabular data. It enables reproducible and systematic testing of Membership Inference Attacks (MIAs) across diverse datasets and six different serialization formats.

  📋 Overview

Datasets:

WTQ (WikiTableQuestions)
WikiSQL
TabFact
Adult Census
California Housing… See the full description on the dataset page: https://huggingface.co/datasets/germane/Tab-MIA.

In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii) a more complex discriminative restricted Boltzmann machine (DRBM) classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii) a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammo...