Thyroid ultrasound diagnosis improvement via multi-view self-supervised
  learning and two-stage pre-training
Jian Wang Xin Yang Xiaohong Jia Wufeng Xue Rusi Chen Yanlin Chen Xiliang Zhu Lian Liu Yan Cao Jianqiao Zhou Dong Ni Ning Gu
Thyroid nodule classification and segmentation in ultrasound images are
crucial for computer-aided diagnosis; however, they face limitations owing to
insufficient labeled data. In this study, we proposed a multi-view contrastive
self-supervised method to improve thyroid nodule classification and
segmentation performance with limited manual labels. Our method aligns the
transverse and longitudinal views of the same nodule, thereby enabling the
model to focus more on the nodule area. We designed an adaptive loss function
that eliminates the limitations of the paired data. Additionally, we adopted a
two-stage pre-training to exploit the pre-training on ImageNet and thyroid
ultrasound images. Extensive experiments were conducted on a large-scale
dataset collected from multiple centers. The results showed that the proposed
method significantly improves nodule classification and segmentation
performance with limited manual labels and outperforms state-of-the-art
self-supervised methods. The two-stage pre-training also significantly exceeded
ImageNet pre-training.