859 research outputs found
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
Deep learning approach for breast cancer diagnosis
Breast cancer is one of the leading fatal disease worldwide with high risk
control if early discovered. Conventional method for breast screening is x-ray
mammography, which is known to be challenging for early detection of cancer
lesions. The dense breast structure produced due to the compression process
during imaging lead to difficulties to recognize small size abnormalities.
Also, inter- and intra-variations of breast tissues lead to significant
difficulties to achieve high diagnosis accuracy using hand-crafted features.
Deep learning is an emerging machine learning technology that requires a
relatively high computation power. Yet, it proved to be very effective in
several difficult tasks that requires decision making at the level of human
intelligence. In this paper, we develop a new network architecture inspired by
the U-net structure that can be used for effective and early detection of
breast cancer. Results indicate a high rate of sensitivity and specificity that
indicate potential usefulness of the proposed approach in clinical use
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 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
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