Aleksandar Vakanski, Min Xian, Phoebe Freer
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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Medical Image Segmentation | BUS 2017 Dataset B | Dice Score | 0.7341 | Salient Attention U-Net |
| Semantic Segmentation | BUS 2017 Dataset B | Dice Score | 0.7341 | Salient Attention U-Net |
| 10-shot image generation | BUS 2017 Dataset B | Dice Score | 0.7341 | Salient Attention U-Net |