2,003 research outputs found
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods rely on regions of interest (ROIs) which
require great efforts to annotate. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
(MIL) for labeling a set of instances/patches, we propose end-to-end trained
deep multi-instance networks for mass classification based on whole mammogram
without the aforementioned ROIs. We explore three different schemes to
construct deep multi-instance networks for whole mammogram classification.
Experimental results on the INbreast dataset demonstrate the robustness of
proposed networks compared to previous work using segmentation and detection
annotations.Comment: MICCAI 2017 Camera Read
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approaches, we apply deep convolutional neural networks (CNN) for
automatic feature learning and classifier building. In computer-aided
mammography, deep CNN classifiers cannot be trained directly on full mammogram
images because of the loss of image details from resizing at input layers.
Instead, our classifiers are trained on labelled image patches and then adapted
to work on full mammogram images for localizing the abnormalities.
State-of-the-art deep convolutional neural networks are compared on their
performance of classifying the abnormalities. Experimental results indicate
that VGGNet receives the best overall accuracy at 92.53\% in classifications.
For localizing abnormalities, ResNet is selected for computing class activation
maps because it is ready to be deployed without structural change or further
training. Our approach demonstrates that deep convolutional neural network
classifiers have remarkable localization capabilities despite no supervision on
the location of abnormalities is provided.Comment: 6 page
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Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.
BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection
A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring
Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal − more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Mammography remains the most prevalent imaging tool for early breast cancer
screening. The language used to describe abnormalities in mammographic reports
is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a
correct BI-RADS category to each examined mammogram is a strenuous and
challenging task for even experts. This paper proposes a new and effective
computer-aided diagnosis (CAD) system to classify mammographic masses into four
assessment categories in BI-RADS. The mass regions are first enhanced by means
of histogram equalization and then semiautomatically segmented based on the
region growing technique. A total of 130 handcrafted BI-RADS features are then
extrcated from the shape, margin, and density of each mass, together with the
mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a
modified feature selection method based on the genetic algorithm (GA) is
proposed to select the most clinically significant BI-RADS features. Finally, a
back-propagation neural network (BPN) is employed for classification, and its
accuracy is used as the fitness in GA. A set of 500 mammogram images from the
digital database of screening mammography (DDSM) is used for evaluation. Our
system achieves classification accuracy, positive predictive value, negative
predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%,
and 79.3%, respectively. To our best knowledge, this is the best current result
for BI-RADS classification of breast masses in mammography, which makes the
proposed system promising to support radiologists for deciding proper patient
management based on the automatically assigned BI-RADS categories
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