2 research outputs found
Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images
Incorporating human domain knowledge for breast tumor diagnosis is
challenging, since shape, boundary, curvature, intensity, or other common
medical priors vary significantly across patients and cannot be employed. This
work proposes a new approach for integrating visual saliency into a deep
learning model for breast tumor segmentation in ultrasound images. Visual
saliency refers to image maps containing regions that are more likely to
attract radiologists visual attention. The proposed approach introduces
attention blocks into a U-Net architecture, and learns feature representations
that prioritize spatial regions with high saliency levels. The validation
results demonstrate increased accuracy for tumor segmentation relative to
models without salient attention layers. The approach achieved a Dice
similarity coefficient of 90.5 percent on a dataset of 510 images. The salient
attention model has potential to enhance accuracy and robustness in processing
medical images of other organs, by providing a means to incorporate
task-specific knowledge into deep learning architectures.Comment: 16 pages, 5 figure
Automatic Breast Ultrasound Image Segmentation: A Survey
Breast cancer is one of the leading causes of cancer death among women
worldwide. In clinical routine, automatic breast ultrasound (BUS) image
segmentation is very challenging and essential for cancer diagnosis and
treatment planning. Many BUS segmentation approaches have been studied in the
last two decades, and have been proved to be effective on private datasets.
Currently, the advancement of BUS image segmentation seems to meet its
bottleneck. The improvement of the performance is increasingly challenging, and
only few new approaches were published in the last several years. It is the
time to look at the field by reviewing previous approaches comprehensively and
to investigate the future directions. In this paper, we study the basic ideas,
theories, pros and cons of the approaches, group them into categories, and
extensively review each category in depth by discussing the principles,
application issues, and advantages/disadvantages.Comment: 40 pages, 6 tables, 180 reference