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

28 citations · 30 across the 5 of their papers we have counts for

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5 papers

eess.IV2022

FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

Kai-Ni Wang, Yuting He, Shuaishuai Zhuang +6

Reliable automatic classification of colonoscopy images is of great significance in assessing the stage of colonic lesions and formulating appropriate treatment plans. However, it…

cs.CV2022

Fine-grained Correlation Loss for Regression

Chaoyu Chen, Xin Yang, Ruobing Huang +10

Regression learning is classic and fundamental for medical image analysis. It provides the continuous mapping for many critical applications, like the attribute estimation, object…

eess.IV2022★ 1 cited

AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images

Kai-Ni Wang, Xin Yang, Juzheng Miao +7

Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentati…

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

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…