Border Sensitive Network in Weakly Supervised Thyroid Nodule Detection for Ultrasound Image

Lesion detection is one of the most important issues in the field of ultrasound image analysis. Recent studies exploit convolutional neural networks to either classify the nodules or locate them with bounding boxes (detection-based algorithms). However, the training of these methods demand considerable and laborious object-level annotations labeled by ultrasound experts and may lack the explicit border information for accurate localization. To reduce the annotation cost and improve the borders feature of proposal box, we propose a novel Border Sensitive Network (BSNet) that extract border features from the extreme point of the borders in proposal box to enhance the single point feature and use a vector to represent arbitrary distribution of border locations. Experimental results demonstrate that BSNet achieves state-of-the-art performance compared with other weakly supervised methods in Thyroid Ultrasound Images dataset, and further analysis reflects their value in clinical practice.