This dataset is a preprocessed version of the original MIAS (Mammographic Image Analysis Society) dataset. It contains 1,679 images with the labels: - normal (0) - benign (1) - malignant (2).

All images were preprocessed by removing artifacts, such as labels and enhancing the images using CLAHE (Contrast Limited AHE). For abnormal images (benign and malignant), the region of interest (ROI) was extracted using the x/y coordinates and radius provided by the original MIAS dataset, and a central breast area was used for normal images.

All training images were augmented to increase the dataset size by using rotation (90°, 180°, 270°), vertical flipping, random bightness and contrast changes, augmenting the training data by a factor of 16. Finally, the training dataset was balanced, resulting in 528 training images per class.

The dataset consists of a total of 1584 training images, 47 validation images, and 48 testing images.

The images were resized to 224 x 224 pixels and are available in .npy format.

The original authors are Suckling et al. (2015) and a modified version, published on https://www.kaggle.com/datasets/kmader/mias-mammography was used to create this dataset.

The dataset was obtained under the CC BY 2.0 license (https://creativecommons.org/licenses/by/2.0/)

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.

Mias Dataset

## Overview

Mias Dataset is a dataset for object detection tasks - it contains ARCH ASYM CIRC MISC SPIC CALC annotations for 318 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).

Dataset

This dataset was created by Qx Nam

Contents

Concise Column Descriptions:

1. MIAS No: MIAS database reference number.
2. BG (Background Tissue): Type of background tissue: - F: Fatty - G: Fatty-glandular - D: Dense-glandular
3. CLASS: Type of abnormality present: - CALC: Calcification - CIRC: Well-defined/circumscribed masses - SPIC: Spiculated masses - MISC: Other, ill-defined masses - ARCH: Architectural distortion - ASYM: Asymmetry - NORM: Normal
4. SEVERITY: Severity of abnormality: - B: Benign - M: Malignant
5. (5-6) x, y Coordinates: Coordinates of the center of the abnormality.
6. Radius (pixels): Approximate radius of the circle enclosing the abnormality.

Descriptions for Your Additional Columns:

• DENSITY: Tissue density classification, indicated in mammogram images as A (low density), B, C, or D (high density).
• BI-RADS: BI-RADS classification used for assessing mammographic abnormalities, e.g., BI-RADS 1 for normal, BI-RADS 5 for highly suspicious of malignancy.
• Group: General classification category (e.g., Normal, Masses, Calcification).

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.

MIAS Mammograms

## Overview

MIAS Mammograms is a dataset for object detection tasks - it contains Mammography Abnormalities 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).

Mini MIAS (obj3)

## Overview

Mini MIAS (obj3) is a dataset for classification tasks - it contains Mammograms 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

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).

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.

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.

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,...)

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).

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

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

Dataset

This dataset was created by Axith Choudhary

Contents

Dataset

This dataset was created by ismail bilal

Contents

Mias Fashion Mfg Co Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

ObjectiveThis study aims to develop a machine learning-based risk prediction model (RPM) for the rupture of multiple intracranial aneurysms (MIAs), addressing a critical gap in current clinical tools such as the PHASES score, which are not specifically designed for MIAs. By analyzing detailed morphological and anatomical parameters, our model provides a tailored approach to rupture risk assessment in MIAs, offering potential improvements over existing methods.MethodsTo address dataset imbalance, we conducted five-fold cross-validation. External validation was not feasible due to data limitations, but we rigorously evaluated model performance using metrics such as accuracy (ACC), true positive rate (TPR), true negative rate (TNR), F1 score, and area under the receiver operating characteristic curve (AUC).ResultsNinety-one patients with 222 aneurysms were recruited, with a rupture rate of 20.3%. The model demonstrated preferable predication performance in unruptured aneurysms (TNR: 0.837) but showed limitations in predicting ruptured aneurysms (TPR: 0.644). Error analysis revealed that the model’s lower TPR may be attributed to the small sample size and dataset imbalance. Overall, the model achieved an accuracy of 0.797 and an AUC of 0.843.ConclusionOur model provides a novel approach to predicting rupture risk in MIAs, complementing existing tools like the PHASES score. However, its clinical applicability is currently limited by suboptimal performance for ruptured aneurysms, which is more suited for identifying MIAs after rupture rather than predicting future rupture risk, and the lack of external validation. Future studies with larger, prospective cohorts are needed to validate and refine the model. This work highlights the potential of machine learning to enhance rupture risk assessment in MIAs, offering a foundation for more personalized treatment strategies.SignificanceMultiple intracranial aneurysms have distinct mechanisms of formation, progression, and rupture. The widely used PHASES score does not incorporate morphological parameters of aneurysms and is not specifically designed for patients with multiple aneurysms. Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.