Automated mass detection in mammograms using cascaded deep learning and random forests
N Dhungel, G Carneiro… - … international conference on …, 2015 - ieeexplore.ieee.org
Mass detection from mammograms plays a crucial role as a pre-processing stage for mass
segmentation and classification. The detection of masses from mammograms is considered …
segmentation and classification. The detection of masses from mammograms is considered …
Automatic mass detection in mammograms using deep convolutional neural networks
With recent advances in the field of deep learning, the use of convolutional neural networks
(CNNs) in medical imaging has become very encouraging. The aim of our paper is to …
(CNNs) in medical imaging has become very encouraging. The aim of our paper is to …
Deep structured learning for mass segmentation from mammograms
N Dhungel, G Carneiro… - 2015 IEEE international …, 2015 - ieeexplore.ieee.org
In this paper, we present a novel method for the segmentation of breast masses from
mammograms exploring structured and deep learning. Specifically, using structured support …
mammograms exploring structured and deep learning. Specifically, using structured support …
A deep learning approach for the analysis of masses in mammograms with minimal user intervention
We present an integrated methodology for detecting, segmenting and classifying breast
masses from mammograms with minimal user intervention. This is a long standing problem …
masses from mammograms with minimal user intervention. This is a long standing problem …
Assessment of a novel mass detection algorithm in mammograms
Aims: In this paper an efficient method for detection of masses in mammograms is
implemented. Settings and Design: The proposed mass detector consists of two major steps …
implemented. Settings and Design: The proposed mass detector consists of two major steps …
Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis
In recent years, a deep convolutional neural network (DCNN) has attracted great attention
due to its outstanding performance in recognition of natural images. However, the DCNN …
due to its outstanding performance in recognition of natural images. However, the DCNN …
A novel featureless approach to mass detection in digital mammograms based on support vector machines
R Campanini, D Dongiovanni, E Iampieri… - Physics in Medicine …, 2004 - iopscience.iop.org
In this work, we present a novel approach to mass detection in digital mammograms. The
great variability of the appearance of masses is the main obstacle to building a mass …
great variability of the appearance of masses is the main obstacle to building a mass …
The automated learning of deep features for breast mass classification from mammograms
The classification of breast masses from mammograms into benign or malignant has been
commonly addressed with machine learning classifiers that use as input a large set of hand …
commonly addressed with machine learning classifiers that use as input a large set of hand …
Automatic detection of abnormal mammograms in mammographic images
CC Jen, SS Yu - Expert Systems with Applications, 2015 - Elsevier
This paper proposes a detection method for abnormal mammograms in mammographic
datasets based on the novel abnormality detection classifier (ADC) by extracting a few of …
datasets based on the novel abnormality detection classifier (ADC) by extracting a few of …
False positive reduction in mammographic mass detection using local binary patterns
In this paper we propose a new approach for false positive reduction in the field of
mammographic mass detection. The goal is to distinguish between the true recognized …
mammographic mass detection. The goal is to distinguish between the true recognized …