• Corpus ID: 16423079

BCDR : A BREAST CANCER DIGITAL REPOSITORY

@inproceedings{Lopez2012BCDRA,
  title={BCDR : A BREAST CANCER DIGITAL REPOSITORY},
  author={Miguel Angel Guevara Lopez and N.G. Posada and Daniel C. Moura and Ra{\'u}l Ramos Poll{\'a}n and Mar{\'i}a Gonz{\'a}lez-Valero Jose and F. Saenz Valiente and Cesar Suarez Ortega and Manuel Rubio del Solar and Guillermo D{\'i}az Herrero and A. IsabelM. and Pereira Ramos and Joana Loureiro and Teresa Cardoso Fernandes and Bruno M. Ferreira de Ara{\'u}jo},
  year={2012}
}
This paper outlines the first Portuguese “Breast Cancer Digital Repository” (BCDR-FMR), a comprehensive annotated repository including digital content (digitized film mammography images) and associated metadata (clinical history, segmented lesions BI-RADS classified, image-based descriptors, biopsy proven, etc.). BCDR-FMR establish a novel reference to develop breast cancer computer-aided detection / diagnosis methods and for training medical students, formed physicians and other medical… 
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75 Citations
Highly Influential Citations
13
Background Citations
16
Methods Citations
25

75 Citations

Can We Predict Histological Images Using Mammograms ?

A computer based breast cancer modelling approach is proposed: “the Mammography–Histology–Linking–Model”, which develops a mapping of features between mammographic abnormalities and their histopathological representation, which avoids the needs for further biopsy.

Contralateral asymmetry for breast cancer detection: A CADx approach

Deep Learning Based Mass Detection in Mammograms

  • Zhenjie CaoZhicheng Yang Jie Ma
  • Computer Science, Medicine
    2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
  • 2019
This paper proposes a Computer Aided Detection (CADe) method to automatically detect masses in mammography that combines Faster R-CNN with Feature Pyramid Network, Focal Loss, Non local Neural Network, and NonLocal Neural Network to achieve the optimal mass detection performance.

DenseNet for Breast Tumor Classification in Mammographic Images

A deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images is built to improve the diagnosis and efficiency in the automatic tumor localization through the medical image classification.

An Online Mammography Database with Biopsy Confirmed Types

A database containing two online breast mammographies to enrich the diversity of mammography data and promote the development of relevant fields is constructed.

Deep learning in mammography and breast histology, an overview and future trends

Convolutional neural networks for breast cancer detection in mammography: A survey

A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography

The most effective pre-processing, image enhancement and segmentation concepts proposed for breast boundary and pectoral muscle segmentation are identified and discussed in hopes of aiding the readers with identifying the best possible solutions for these segmentation problems.

An AI-based method to retrieve hematoxylin and eosin breast histology images using mammograms

An AI-based method to describe mass lesions in semantic abstracts/codes is proposed, used for unifying classification and retrieval in a single learning process, while enforcing similar lesion types to have similar semantic codes in a compact form.

Improving the breast cancer diagnosis using digital repositories

The aim is to achieve a reference repository for breast cancer diagnosis with BCDR, improving the existing implementations by storing a large number of annotated diagnosed cases reviewed by specialists, so researchers can have a reliable source of information for their researches.
...

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