6 citations · 6 across the 2 of their papers we have counts for
5 papers
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
Alain Lalande, Zhihao Chen, Thibaut Pommier +30
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization the…
Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation
Yichi Zhang, Jicong Zhang
The success of deep learning methods in medical image segmentation tasks usually requires a large amount of labeled data. However, obtaining reliable annotations is expensive and t…
Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI
Yichi Zhang
Automatic segmentation of myocardial contours and relevant areas like infraction and no-reflow is an important step for the quantitative evaluation of myocardial infarction. In thi…
Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation
Yichi Zhang, Qingcheng Liao, Lin Yuan +3
The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infecti…
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
Jun Ma, Yao Zhang, Song Gu +14
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved…