362 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
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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
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
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
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images
In this study, the main objective is to develop an algorithm capable of
identifying and delineating tumor regions in breast ultrasound (BUS) and
mammographic images. The technique employs two advanced deep learning
architectures, namely U-Net and pretrained SAM, for tumor segmentation. The
U-Net model is specifically designed for medical image segmentation and
leverages its deep convolutional neural network framework to extract meaningful
features from input images. On the other hand, the pretrained SAM architecture
incorporates a mechanism to capture spatial dependencies and generate
segmentation results. Evaluation is conducted on a diverse dataset containing
annotated tumor regions in BUS and mammographic images, covering both benign
and malignant tumors. This dataset enables a comprehensive assessment of the
algorithm's performance across different tumor types. Results demonstrate that
the U-Net model outperforms the pretrained SAM architecture in accurately
identifying and segmenting tumor regions in both BUS and mammographic images.
The U-Net exhibits superior performance in challenging cases involving
irregular shapes, indistinct boundaries, and high tumor heterogeneity. In
contrast, the pretrained SAM architecture exhibits limitations in accurately
identifying tumor areas, particularly for malignant tumors and objects with
weak boundaries or complex shapes. These findings highlight the importance of
selecting appropriate deep learning architectures tailored for medical image
segmentation. The U-Net model showcases its potential as a robust and accurate
tool for tumor detection, while the pretrained SAM architecture suggests the
need for further improvements to enhance segmentation performance
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