2,861 research outputs found
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
Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?
Aim
To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates.
Materials and methods
The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient.
Results
In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud
Conclusion
This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management
Application of Fractal and Wavelets in Microcalcification Detection
Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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