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.

Content

The data is images and labels / annotations for mammography scans. More about the database can be found at MIAS. The 'Preview' kernel shows how the Info.txt and PGM files can be parsed correctly.

Labels

• 1st column: MIAS database reference number.

• 2nd column: Character of background tissue: F Fatty G Fatty-glandular D Dense-glandular

• 3rd column: Class 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

• 4th column: Severity of abnormality; B Benign M Malignant

• 5th, 6th columns: x,y image-coordinates of centre of abnormality.

• 7th column: Approximate radius (in pixels) of a circle enclosing the abnormality. There are also several things you should note:

The list is arranged in pairs of films, where each pair represents the left (even filename numbers) and right mammograms (odd filename numbers) of a single patient. The size of all the images is 1024 pixels x 1024 pixels. The images have been centered in the matrix. When calcifications are present, centre locations and radii apply to clusters rather than individual calcifications. Coordinate system origin is the bottom-left corner. In some cases calcifications are widely distributed throughout the image rather than concentrated at a single site. In these cases centre locations and radii are inappropriate and have been omitted.

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.

1. 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.

2. 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.

3. OTHER RESTRICTIONS You may not rent, lease or sell the DATABASE.

4. 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.

Inspiration

Automatically finding lesions would be a very helpful tool for physicians, also predicting malignancy based on a found/marked lesion

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.

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.

These files contain image pixel coordinates of individual microcalcifications in the images from the mini-MIAS database, available from http://peipa.essex.ac.uk/info/mias.html. The file name corresponds to the particular image name in the mini-MIAS database. See also our webpage at http://cv.snu.ac.kr/research/cascade-mc-detector15/index.html for updated information. For questions regarding data, e-mail: syshin@snu.ac.kr

Dataset

This dataset was created by Tawfiq Beghriche

Contents

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 Mammographic Image Analysis Society (MIAS) and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC) and precision-recall curves for detection of individual μCs and free-response receiver operating characteristic (FROC) curve for detection of clustered μCs.

Dataset

This dataset was created by Qx Nam

Contents

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.

The Test time is the average time for detecting μCs in one image. The time is represented in seconds.

COVID-19 Response -- MD Insurance Administration (description updated 2/6/2023)

This Dataset contains the JPG images of Breast Cancer taken from the CBIS-DDSM.

https://i.imgur.com/rz4rtQI.png" alt="Breast Cancer Images">

Descripton

This dataset contains JPG format images (2.49 GB) of the original CBIS-DDSM dataset (163 GB) which are in DICOM format and by maintaining the same resolution of the images as it was in the original dataset.

The original dataset was split into train and test by having two cases one is Mass and another is Calcification(Calc) i.e. calc_case_description_test_set.csv, calc_case_description_train_set.csv, mass_case_description_test_set.csv, mass_case_description_train_set.csv, and metadata.csv but in here this dataset is made by converting the images from DICOM to JPG format, removing the unnecessary columns by Data Cleaning and concatenating both the Mass and Calcification(Calc) cases train test into one i.e. calc_case(with_jpg_img).csv, mass_case(with_jpg_img).csv, and metadata(with_jpg_img).csv.

| Collection | | | --- | --- | | Number of Studies | 6775 | | Number of Series | 6775 | | Number of Participants | 1,566(NB) | | Number of Images | 10239 | | Modalities | MG | | Image Size (GB) | 6(.jpg) |

NB: The image data for this collection is structured such that each participant has multiple patient IDs. For example, pat_id 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 participants according to the DICOM metadata, but there are only 1,566 actual participants in the cohort.

File Description

1. JPG image folder file structure

https://i.imgur.com/KtBMlVm.png" alt="Cancer Image file structure"> File naming: - Folder name: Subject ID > Study UID > Series UID - File name: Series Description > img_0 > 1.jpg

2. CSV files description

Summary

The 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 a curated and standardized version of the DDSM for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography.

Source of the Original dataset

https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY

Citations & Data Usage Policy

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

CBIS-DDSM Citation

Sawyer-Lee, R., Gimenez, F., Hoogi, A., & Rubin, D. (2016). Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY

Publication Citation

Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017). A curated mammography data set for use in computer-aided detection and diagnosis research. In Scientific Data (Vol. 4, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/sdata.2017.177

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

NOTE: The CBIS-DDSM dataset lack DICOM image for cropped and ROI images, so there are some cropped and ROI images that may not be found corresponding to their given path, so it is recommended to use the full mammography images from this dataset (only)

MIA contributors to the customer service annual report.

Protein-Protein, Genetic, and Chemical Interactions for MIA (Homo sapiens) curated by BioGRID (https://thebiogrid.org); DEFINITION: melanoma inhibitory activity

Dataset

This dataset was created by Nagi Reddy

Released under Other (specified in description)

Contents

The online revenue of mia.vn amounted to US$27.6m in 2023. Discover eCommerce insights, including sales development, shopping cart size, and many more.

Credit report of Puma / Cn Usa Inter / Lotto / Mias Fashion Mfg Co Inc contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Explore Mia Kalef through unique data from multiples sources: key facts, real-time news, interactive charts, detailed maps & open datasets

Monoterpene indole alkaloids (MIAs) are a diverse family of complex plant secondary metabolites with many medicinal properties, including the essential anti-cancer therapeutics vinblastine and vincristine. As MIAs are difficult to chemically synthesize, the world’s supply chain for vinblastine relies on low-yielding extractions of precursors vindoline and catharanthine from the plant Catharanthus roseus, followed by chemical coupling and reduction to form vinblastine. Here, we demonstrate de novo microbial biosynthesis of vindoline and catharanthine from renewable feedstocks such as simple sugar and amino acids using highly engineered yeast. The study showcases the longest biosynthetic pathway refactored into a microbial cell factory to date, including 29 enzymatic steps from the yeast native metabolite geranyl pyrophosphate to catharanthine and vindoline. We made 44 genetic edits to yeast that include expression of 35 heterologous genes from plants as well as deletions, knock-downs, and overexpression of 10 yeast genes or variants thereof to improve the precursor supply. Finally, we demonstrate one-step in vitro vinblastine production using chemical coupling and reduction of vindoline and catharanthine. Not only is the yeast a scalable platform for production of vinblastine, it is also a platform for production of more than 2,000 different natural and new-to-nature MIAs.