Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images

TL;DR

This paper introduces a saliency-enhanced deep learning model for breast tumor segmentation in ultrasound images, improving accuracy by integrating visual attention mechanisms into a U-Net architecture.

Contribution

It presents a novel method of incorporating visual saliency into deep learning models for medical image segmentation, specifically using attention blocks within U-Net.

Findings

Achieved a Dice coefficient of 90.5% on 510 ultrasound images.

Outperformed models without salient attention layers.

Demonstrated potential for application to other medical imaging tasks.

Abstract

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.