1 citations · 2 across the 9 of their papers we have counts for
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Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D
Jiesi Hu, Chenfei Ye, Yanwu Yang +5
In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance f…
Centerline Boundary Dice Loss for Vascular Segmentation
Pengcheng Shi, Jiesi Hu, Yanwu Yang +3
Vascular segmentation in medical imaging plays a crucial role in analysing morphological and functional assessments. Traditional methods, like the centerline Dice (clDice) loss, en…
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation
Xutao Guo, Yanwu Yang, Chenfei Ye +3
Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise…
Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net
Yanwu Yang, Xutao Guo, Yiwei Pan +3
Accurate medical image segmentation is crucial for diagnosis and analysis. However, the models without calibrated uncertainty estimates might lead to errors in downstream analysis…
Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning
Yang Yang, Jiancong Chen, Ruixuan Wang +5
Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images. Due to the nature…