• Corpus ID: 16423079


  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},
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… 

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.

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.

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.



Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis

This work massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal and mediolateral oblique mammography image views, providing BI-RADS diagnosis.

Online Mammographic Images Database for Development and Comparison of CAD Schemes

Comparison with other databases currently available has shown that the presented database has a sufficient number of images, is of high quality, and is the only one to include a functional search system.


An experimental Grid application in Mammography Computer-Aided Diagnosis (CAD) is developed including a general framework that supports all its stages allowing semiautomatic classification of digital mammograms.

Digital Imaging and Communications in Medicine

This chapter provides an insight into a newly developed DICOM service called “Application Hosting”, which introduces a standardized plug-in architecture for image processing, thus permitting users to utilize cross-vendor image processing plug-ins in DICom applications.

The digital imaging and communications in medicine

This Digital Imaging And Communications In Medicine tends to be the representative book in this website.

A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources

The BiomedTK software framework was experimentally validated by training thousands of classifier configurations for representative biomedical UCI datasets reaching in little time classification levels comparable to those reported in existing literature.

Cancro da mama mata 5 mulheres por dia em Portugal

  • CiênciaHoje
  • 2009

Cancro da mama mata 5 mulheres por dia em Portugal”, in: In: (Ed.) CiênciaHoje

  • 2009