465 research outputs found
Deep Structured learning for mass segmentation from Mammograms
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 vector machine (SSVM), we formulate a model that
combines different types of potential functions, including one that classifies
image regions using deep learning. Our main goal with this work is to show the
accuracy and efficiency improvements that these relatively new techniques can
provide for the segmentation of breast masses from mammograms. We also propose
an easily reproducible quantitative analysis to as- sess the performance of
breast mass segmentation methodologies based on widely accepted accuracy and
running time measurements on public datasets, which will facilitate further
comparisons for this segmentation problem. In particular, we use two publicly
available datasets (DDSM-BCRP and INbreast) and propose the computa- tion of
the running time taken for the methodology to produce a mass segmentation given
an input image and the use of the Dice index to quantitatively measure the
segmentation accuracy. For both databases, we show that our proposed
methodology produces competitive results in terms of accuracy and running time.Comment: 4 pages, 2 figure
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with a position priori. Further, we employ
adversarial training to eliminate over-fitting due to the small sizes of
mammogram datasets. Multi-scale FCN is employed to improve the segmentation
performance. Experimental results on two public datasets, INbreast and
DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
than state-of-the-art approaches.
\footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with
arXiv:1612.0597
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
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
Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography
We propose new methods for the prediction of 5-year mortality in elderly
individuals using chest computed tomography (CT). The methods consist of a
classifier that performs this prediction using a set of features extracted from
the CT image and segmentation maps of multiple anatomic structures. We explore
two approaches: 1) a unified framework based on deep learning, where features
and classifier are automatically learned in a single optimisation process; and
2) a multi-stage framework based on the design and selection/extraction of
hand-crafted radiomics features, followed by the classifier learning process.
Experimental results, based on a dataset of 48 annotated chest CTs, show that
the deep learning model produces a mean 5-year mortality prediction accuracy of
68.5%, while radiomics produces a mean accuracy that varies between 56% to 66%
(depending on the feature selection/extraction method and classifier). The
successful development of the proposed models has the potential to make a
profound impact in preventive and personalised healthcare.Comment: 9 page
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