11 datasets found
  1. c

    Curated Breast Imaging Subset of Digital Database for Screening Mammography

    • cancerimagingarchive.net
    csv, dicom, n/a
    Updated Sep 14, 2017
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    The Cancer Imaging Archive (2017). Curated Breast Imaging Subset of Digital Database for Screening Mammography [Dataset]. http://doi.org/10.7937/K9/TCIA.2016.7O02S9CY
    Explore at:
    csv, dicom, n/aAvailable download formats
    Dataset updated
    Sep 14, 2017
    Dataset authored and provided by
    The Cancer Imaging Archive
    License

    https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/

    Time period covered
    Sep 14, 2017
    Dataset funded by
    National Cancer Institutehttp://www.cancer.gov/
    Description

    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.

  2. CBIS-DDSM: Breast Cancer Image Dataset

    • kaggle.com
    Updated Feb 7, 2021
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    Awsaf (2021). CBIS-DDSM: Breast Cancer Image Dataset [Dataset]. https://www.kaggle.com/awsaf49/cbis-ddsm-breast-cancer-image-dataset/tasks
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 7, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Awsaf
    License

    Attribution-ShareAlike 3.0 (CC BY-SA 3.0)https://creativecommons.org/licenses/by-sa/3.0/
    License information was derived automatically

    Description

    https://www.researchgate.net/publication/338558131/figure/fig3/AS:962412517793792@1606468433025/CBIS-DDSM-example-images-used-for-detection.jpg" alt="">

    Descripton

    This dataset is jpeg format of the original dataset(163GB). The resolution was kept to the original dataset.

    • 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.

    Summary

    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 DDDSM 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.

    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 inquiries about this dataset, please contact Dr. Daniel Rubin, Department of Biomedical Data Science, Radiology, and Medicine, Stanford University School of Medicine (dlrubin@stanford.edu).

    Citations & Data Usage Policy

    Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations:

    CBIS-DDSM Citation

     Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi , Daniel Rubin (2016). **Curated Breast Imaging Subset of DDSM [Dataset]**. The Cancer Imaging Archive. **DOI:** https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY
    

    Publication Citation

    Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Kanae Kawai Miyake, Mia Gorovoy & Danie...
    
  3. D

    CBIS-DDSM Dataset

    • datasetninja.com
    Updated Sep 14, 2017
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    Rebecca Sawyer Lee; Francisco Gimenez; Assaf Hoogi (2017). CBIS-DDSM Dataset [Dataset]. https://datasetninja.com/cbis-ddsm
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    Dataset updated
    Sep 14, 2017
    Dataset provided by
    Dataset Ninja
    Authors
    Rebecca Sawyer Lee; Francisco Gimenez; Assaf Hoogi
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    The CBIS-DDSM: Curated Breast Imaging Subset of Digital Database for Screening Mammography includes decompressed images, data selection and curation by trained mammographers, updated mass segmentation and bounding boxes, and pathologic diagnosis for training data, formatted similarly to modern computer vision data sets. The data set contains 753 calcification cases and 891 mass cases, providing a data set size capable of analyzing decision support systems in mammography.

  4. t

    Digital Database for Screening Mammography (DDSM) dataset - Dataset - LDM

    • service.tib.eu
    Updated Dec 2, 2024
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    (2024). Digital Database for Screening Mammography (DDSM) dataset - Dataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/digital-database-for-screening-mammography--ddsm--dataset
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    Dataset updated
    Dec 2, 2024
    Description

    The DDSM dataset is a public mammogram dataset used for training and testing the proposed method.

  5. CBIS-DDSM: Breast Cancer Dataset of JPG Images

    • kaggle.com
    Updated Jan 24, 2023
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    Debjeet Das (2023). CBIS-DDSM: Breast Cancer Dataset of JPG Images [Dataset]. https://www.kaggle.com/datasets/debjeetdas/breast-cancer-jpg-image-dataset-of-cbisddsm
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 24, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Debjeet Das
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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

    CSV FileDescription
    calc_case(with_jpg_img).csvThis file contains the Calcification cases patients with their patient_id, breast_density, left or right breast, image view, abnormality id, abnormality type, mass shape, mass margins, assessment, pathology, subtlety, jpg_fullMammo_img_path, jpg_crop_img_path, jpg_ROI_img_path
    mass_case(with_jpg_img).csvThis file contains the Mass cases patients with their patient_id, breast_density, left or right breast, image view, abnormality id, abnormality type, mass shape, mass margins, assessment, pathology, subtlety, jpg_fullMammo_img_path, jpg_crop_img_path, jpg_ROI_img_path
    metadata(with_jpg_img).csvThis file contains both of the Mass and Calcification(Calc) patients with their Series UID, Subject ID, Study UID, Series Description, Modality, SOP Class Name, SOP Class UID, Number of Images, jpg_folder_path



    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 ...

  6. m

    Breast Mammography Image Dataset with Masses

    • data.mendeley.com
    Updated Jan 27, 2023
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    David Faramonna (2023). Breast Mammography Image Dataset with Masses [Dataset]. http://doi.org/10.17632/8fztxggjnc.1
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    Dataset updated
    Jan 27, 2023
    Authors
    David Faramonna
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  7. Mini-DDSM

    • kaggle.com
    Updated Nov 18, 2020
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    Abbas Cheddad (2020). Mini-DDSM [Dataset]. https://www.kaggle.com/cheddad/miniddsm/code
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 18, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Abbas Cheddad
    Description

    ::. Update (2020-11-18) .:: 👍 A larger and complete Mini-DDSM data set is uploaded, you can get it from:

    https://www.kaggle.com/cheddad/miniddsm2

    Consent

    By downloading the Mini-DDSM Data Set, you agree to the following::

    1. This page on Kaggle remains the main source of the data set (i.e., no redistribution of this data set)
    2. In any resultant publications of research that uses the paper / data set, due credits must be provided to: C.D. Lekamlage, F. Afzal, E. Westerberg and A. Cheddad, “Mini-DDSM: Mammography-based Automatic Age Estimation,” in the 3rd International Conference on Digital Medicine and Image Processing (DMIP 2020), ACM, Kyoto, Japan, November 06-09, 2020.

    Context

    You can read the Paper that describes this free data set. The intention here is to make an easy access to the DDSM (low resolution though) data set along with their age/density attributes.

    Content

    This is the light-weight version of the popular DDSM (Digital Database for Screening Mammography) [Ref] data set which currently is obsolete. To answer the nagging question why Mini-DDSM, it is important to know that the DDSM database has a website maintained at the University of South Florida for purposes of keeping it accessible on the web. However, image files are compressed with lossless JPEG (i.e., “.LJPEG”) encoding that are generated using a broken software (or at least an outdated tool as described on the DDSM website). CBIS-DDSM provides an alternative host of the original DDSM, but unfortunately, images are stripped from their original identification filename and from the age attribute. Figure 1 illustrates the different classes Mini-DDSM exhibits.

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1822946%2Facc222dc42dad22cd24656f20e4ef2ab%2FFig.png?generation=1601633739188706&alt=media" alt="">

    Figure 1. Age distribution in the Mini-DDSM data set stratified by patient's status.

    Inspiration

    Please give us feedback/suggestions to improve the data set to "abbas DOT cheddad AT bth DOT se"

    [Ref]: The Digital Database for Screening Mammography, Michael Heath, Kevin Bowyer, Daniel Kopans, Richard Moore and W. Philip Kegelmeyer, in Proceedings of the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed., 212-218, Medical Physics Publishing, 2001. ISBN 1-930524-00-5.

  8. f

    Data distribution of CBIS-DDSM dataset.

    • plos.figshare.com
    xls
    Updated Jul 11, 2024
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    Jawad Ahmad; Sheeraz Akram; Arfan Jaffar; Zulfiqar Ali; Sohail Masood Bhatti; Awais Ahmad; Shafiq Ur Rehman (2024). Data distribution of CBIS-DDSM dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0304757.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jul 11, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Jawad Ahmad; Sheeraz Akram; Arfan Jaffar; Zulfiqar Ali; Sohail Masood Bhatti; Awais Ahmad; Shafiq Ur Rehman
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system’s exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method’s performance was approximately 95.39%. Upon completing all the analysis, the system’s classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

  9. f

    Approaches comparison on CBIS DDSM dataset.

    • plos.figshare.com
    xls
    Updated Oct 2, 2024
    + more versions
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    Mudassar Ali; Tong Wu; Haoji Hu; Tariq Mahmood (2024). Approaches comparison on CBIS DDSM dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0309421.t005
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    xlsAvailable download formats
    Dataset updated
    Oct 2, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Mudassar Ali; Tong Wu; Haoji Hu; Tariq Mahmood
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  10. f

    Evaluating the identification of mass lesions.

    • plos.figshare.com
    xls
    Updated Jul 11, 2024
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    Jawad Ahmad; Sheeraz Akram; Arfan Jaffar; Zulfiqar Ali; Sohail Masood Bhatti; Awais Ahmad; Shafiq Ur Rehman (2024). Evaluating the identification of mass lesions. [Dataset]. http://doi.org/10.1371/journal.pone.0304757.t010
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jul 11, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Jawad Ahmad; Sheeraz Akram; Arfan Jaffar; Zulfiqar Ali; Sohail Masood Bhatti; Awais Ahmad; Shafiq Ur Rehman
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system’s exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method’s performance was approximately 95.39%. Upon completing all the analysis, the system’s classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

  11. f

    Comparison of the method performance with existing general object detection....

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Jingzhen He; Jing Wang; Zeyu Han; Baojun Li; Mei Lv; Yunfeng Shi (2023). Comparison of the method performance with existing general object detection. [Dataset]. http://doi.org/10.1371/journal.pone.0275194.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Jingzhen He; Jing Wang; Zeyu Han; Baojun Li; Mei Lv; Yunfeng Shi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Comparison of the method performance with existing general object detection.

  12. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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The Cancer Imaging Archive (2017). Curated Breast Imaging Subset of Digital Database for Screening Mammography [Dataset]. http://doi.org/10.7937/K9/TCIA.2016.7O02S9CY

Curated Breast Imaging Subset of Digital Database for Screening Mammography

CBIS-DDSM

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231 scholarly articles cite this dataset (View in Google Scholar)
csv, dicom, n/aAvailable download formats
Dataset updated
Sep 14, 2017
Dataset authored and provided by
The Cancer Imaging Archive
License

https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/

Time period covered
Sep 14, 2017
Dataset funded by
National Cancer Institutehttp://www.cancer.gov/
Description

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.

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