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Elicitation and representation of expert knowledge for computer aided diagnosis in mammography
To study how professional radiologists describe, interpret and make decisions about micro-calcifications in mammograms. The purpose was to develop a model of the radiologists' decision making for use in CADMIUM II, a computerized aid for mammogram interpretation that combines symbolic reasoning with image processing
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
Healthcare technologies and professional vision
This paper presents some details from an observational evaluation of a computer assisted detection tool in mammography. The use of the tool, its strengths and weaknesses, are documented and its impact on reader's 'professional vision' (Goodwin 1994) considered. The
paper suggests issues for the design, use and, importantly, evaluation of new technologies in
everyday medical work, pointing to general issues concerning trust – users’ perception of the dependability of the evidence generated by such tools and suggesting that evaluations require an emphasis on the complex issue of what technologies afford their users in everyday work
Tumor Prediction in Mammogram using Neural Network
Detecting micro calcifications - early breast cancer indicators 2013; is visually tough while recognizing malignant tumors is a highly complicated issue. Digital mammography ensures early breast cancer detection through digital mammograms locating suspicious areas with benign/- malignant micro calcifications. Early detection is vital in treatment and survival of breast cancer as there are no sure ways to prevent it. This paper presents a method of tumor prediction based on extracting features from mammogram using Gabor filter with Discrete cosine transform and classify the features using Neural Network
A scalable Computer-Aided Detection system for microcalcification cluster identification in a pan-European distributed database of mammograms
A computer-aided detection (CADe) system for microcalcification cluster
identification in mammograms has been developed in the framework of the
EU-founded MammoGrid project. The CADe software is mainly based on wavelet
transforms and artificial neural networks. It is able to identify
microcalcifications in different kinds of mammograms (i.e. acquired with
different machines and settings, digitized with different pitch and bit depth
or direct digital ones). The CADe can be remotely run from GRID-connected
acquisition and annotation stations, supporting clinicians from geographically
distant locations in the interpretation of mammographic data. We report the
FROC analyses of the CADe system performances on three different dataset of
mammograms, i.e. images of the CALMA INFN-founded database collected in the
Italian National screening program, the MIAS database and the so-far collected
MammoGrid images. The sensitivity values of 88% at a rate of 2.15 false
positive findings per image (FP/im), 88% with 2.18 FP/im and 87% with 5.7 FP/im
have been obtained on the CALMA, MIAS and MammoGrid database respectively.Comment: 6 pages, 5 figures; Proceedings of the ITBS 2005, 3rd International
Conference on Imaging Technologies in Biomedical Sciences, 25-28 September
2005, Milos Island, Greec
Computer aided detection in mammography
Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201
Detection of microcalcifications in mammograms using error of prediction and statistical measures
A two-stage method for detecting microcalcifications in
mammograms is presented. In the first stage, the determination of
the candidates for microcalcifications is performed. For this purpose,
a 2-D linear prediction error filter is applied, and for those pixels
where the prediction error is larger than a threshold, a statistical
measure is calculated to determine whether they are candidates for
microcalcifications or not. In the second stage, a feature vector is
derived for each candidate, and after a classification step using a
support vector machine, the final detection is performed. The algorithm
is tested with 40 mammographic images, from Screen Test:
The Alberta Program for the Early Detection of Breast Cancer with
50- m resolution, and the results are evaluated using a freeresponse
receiver operating characteristics curve. Two different
analyses are performed: an individual microcalcification detection
analysis and a cluster analysis. In the analysis of individual microcalcifications,
detection sensitivity values of 0.75 and 0.81 are obtained
at 2.6 and 6.2 false positives per image, on the average,
respectively. The best performance is characterized by a sensitivity
of 0.89, a specificity of 0.99, and a positive predictive value of 0.79.
In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77
false positives per image, and a value of 0.90 is achieved at 0.94
false positive per imag
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