activity
20202022
most citedLearn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

28 citations · 34 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022

HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images

Jikuan Qian, Rui Li, Xin Yang +8

Different from handcrafted features, deep neural networks can automatically learn task-specific features from data. Due to this data-driven nature, they have achieved remarkable su…

cs.CV2021★ 4 cited

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

Xin Yang, Yuhao Huang, Ruobing Huang +10

3D ultrasound (US) has become prevalent due to its rich spatial and diagnostic information not contained in 2D US. Moreover, 3D US can contain multiple standard planes (SPs) in one…

cs.CV2021★ 28 cited

Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

Guang-Quan Zhou, Juzheng Miao, Xin Yang +7

Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have imp…

eess.IV2020★ 1 cited

Contrastive Rendering for Ultrasound Image Segmentation

Haoming Li, Xin Yang, Jiamin Liang +12

Ultrasound (US) image segmentation embraced its significant improvement in deep learning era. However, the lack of sharp boundaries in US images still remains an inherent challenge…

eess.IV2020★ 1 cited

Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound

Wang Jian, Miao Juzheng, Yang Xin +9

Breast cancer is the most common invasive cancer in women. Besides the primary B-mode ultrasound screening, sonographers have explored the inclusion of Doppler, strain and shear-wa…