Masked Video Modeling with Correlation-aware Contrastive Learning for
Breast Cancer Diagnosis in Ultrasound
- URL: http://arxiv.org/abs/2208.09881v1
- Date: Sun, 21 Aug 2022 13:23:32 GMT
- Title: Masked Video Modeling with Correlation-aware Contrastive Learning for
Breast Cancer Diagnosis in Ultrasound
- Authors: Zehui Lin, Ruobing Huang, Dong Ni, Jiayi Wu, Baoming Luo
- Abstract summary: We propose a pioneering approach that directly utilizes US videos in computer-aided breast cancer diagnosis.
It leverages masked video modeling as pretraning to reduce reliance on dataset size and detailed annotations.
Experimental results show that our proposed approach achieved promising classification performance and can outperform other state-of-the-art methods.
- Score: 7.957750764582688
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Breast cancer is one of the leading causes of cancer deaths in women. As the
primary output of breast screening, breast ultrasound (US) video contains
exclusive dynamic information for cancer diagnosis. However, training models
for video analysis is non-trivial as it requires a voluminous dataset which is
also expensive to annotate. Furthermore, the diagnosis of breast lesion faces
unique challenges such as inter-class similarity and intra-class variation. In
this paper, we propose a pioneering approach that directly utilizes US videos
in computer-aided breast cancer diagnosis. It leverages masked video modeling
as pretraning to reduce reliance on dataset size and detailed annotations.
Moreover, a correlation-aware contrastive loss is developed to facilitate the
identifying of the internal and external relationship between benign and
malignant lesions. Experimental results show that our proposed approach
achieved promising classification performance and can outperform other
state-of-the-art methods.
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