# Deep learning for breast ultrasound tumor detection

> **NIH NIH P20** · UNIVERSITY OF IDAHO · 2021 · $181,902

## Abstract

Breast cancer is one of the leading causes of death in females. Early detection of breast tumors is critical to
increasing the survival of women diagnosed with this disease. Accurate computer-aided detection of breast
tumors could improve early detection but requires segmentation, a process that provides the precise tumor
location, size, boundary, and shape. Existing breast tumor segmentation approaches are sensitive to small
changes in image quality (e.g., intensity, contrast, noise, artifacts), limiting their application in early detection of
breast cancer. The goal of the proposed project is to overcome current limitations by building tumor
segmentation methodologies that are robust to variations of image quality. We will use breast ultrasound
images due to the noninvasive, painless, nonradioactive, and cost-effective nature of the imaging procedure.
We propose the following specific aims to achieve this goal. (1) Model human breast anatomy. In clinical
examination, the knowledge of breast anatomy helps radiologists distinguish between breast tissues. In this
aim, we will develop a graphical model to represent the spatial relationship of different breast layers and to
help distinguish tumor regions from normal regions. We will develop a new mathematical tool called tissue
connectedness for modeling breast anatomy in ultrasound images. Tissue connectedness allows for the
identification of different breast tissues and helps distinguish a breast tumor from normal tumor-like regions
(e.g., artifacts, fat). (2) Model the visual saliency of breast tumors. Visual saliency is a property that makes an
object in images stand out from neighboring objects. We will overcome the invalid assumption made in
previous approaches that there is at least one tumor in the image by developing a robust model for estimating
visual saliency of breast tumors. With the help of this model, we will detect all possible tumor regions that
would attract a radiologist’s attention, with no output of salient regions when no tumor exists in an image. (3)
Develop a domain-enriched deep learning framework for tumor segmentation. A deep learning-based
framework will be developed to integrate the output of models from Aims 1 and 2 and will lead to an overall
model that segments breast tumors. We will train and test the approach using 1800 breast ultrasound images
from four medical schools collected using five different ultrasound devices. Seven quantitative metrics will be
applied to evaluate the performance of the proposed segmentation approach. Discrepancies between
computational and manual tumor segmentation will be used to refine the models. Success of the proposed
project will enhance methodologies for robust and reproducible breast ultrasound image segmentation and
broaden the use of computer-aided diagnosis for early detection of breast cancer.

## Key facts

- **NIH application ID:** 10220059
- **Project number:** 5P20GM104420-07
- **Recipient organization:** UNIVERSITY OF IDAHO
- **Principal Investigator:** Min Xian
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $181,902
- **Award type:** 5
- **Project period:** 2015-03-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10220059

## Citation

> US National Institutes of Health, RePORTER application 10220059, Deep learning for breast ultrasound tumor detection (5P20GM104420-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10220059. Licensed CC0.

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