20 citations · 78 across the 11 of their papers we have counts for
7 papers · 1 filter
Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey
Yichi Zhang, Rushi Jiao
Due to the flexibility of prompting, foundation models have become the dominant force in the domains of natural language processing and image generation. With the recent introducti…
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation
Yichi Zhang, Rushi Jiao, Qingcheng Liao +2
Medical image segmentation is a fundamental and critical step in many clinical approaches. Semi-supervised learning has been widely applied to medical image segmentation tasks sinc…
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…
Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions
Yichi Zhang, Qingcheng Liao, Le Ding +1
Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as…