Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers

Autor:	Sun, AnLan, Zhang, Zhao, Lei, Meng, Dai, Yuting, Wang, Dong, Wang, Liwei
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Breast ultrasound videos contain richer information than ultrasound images, therefore it is more meaningful to develop video models for this diagnosis task. However, the collection of ultrasound video datasets is much harder. In this paper, we explore the feasibility of enhancing the performance of ultrasound video classification using the static image dataset. To this end, we propose KGA-Net and coherence loss. The KGA-Net adopts both video clips and static images to train the network. The coherence loss uses the feature centers generated by the static images to guide the frame attention in the video model. Our KGA-Net boosts the performance on the public BUSV dataset by a large margin. The visualization results of frame attention prove the explainability of our method. The codes and model weights of our method will be made publicly available. Comment: Medical Image Computing and Computer-Assisted Intervention 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.06877 Zobrazit plný text záznamu View this record from Arxiv