HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images

Author:

Mo Yuhao1,Han Chu2ORCID,Liu Yu3,Liu Min4ORCID,Shi Zhenwei3ORCID,Lin Jiatai5,Zhao Bingchao1,Huang Chunwang6ORCID,Qiu Bingjiang3,Cui Yanfen3,Wu Lei3,Pan Xipeng3,Xu Zeyan3,Huang Xiaomei3ORCID,Li Zhenhui3,Liu Zaiyi3,Wang Ying7,Liang Changhong3

Affiliation:

1. School of Medicine, South China University of Technology, Guangzhou, China

2. Department of Radiology, Medical Research Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China

3. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China

4. Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China

5. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

6. Department of Ultrasound, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China

7. Department of Medical Ultrasonics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

Funder

Key-Area Research and Development Program of Guangdong Province

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

National Key Research and Development Program of China

National Science Fund for Distinguished Young Scholars

Regional Innovation and Development Joint Fund of National Natural Science Foundation of China

National Science Foundation for Young Scientists of China

National Natural Science Foundation of China

High-level Hospital Construction Project of Guangdong Provincial People's Hospital

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Reference43 articles.

1. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

2. COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models;gao;arXiv 2107 01682,2021

3. MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data

4. Transformers in medical imaging: A survey;shamshad;arXiv 2201 09873,2022

5. Methods for the segmentation and classification of breast ultrasound images: a review

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