1 citations · 2 across the 10 of their papers we have counts for
11 papers
UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
Yunzhou Li, Jiesi Hu, Yanwu Yang +5
Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fragmented by prompt paradigms and…
Controllable Histopathology Image Synthesis with Training-free Structural Initialization and Textural Modulation
Yuheng Qiu, Jingyi Luo, Chenfei Ye +2
Deep learning has demonstrated remarkable success in high-throughput histopathology image analysis. However, the performance of learning-based models critically depends on the qual…
Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning
Jiesi Hu, Yanwu Yang, Zhiyu Ye +4
Universal models for medical image segmentation, such as interactive and in-context learning (ICL) models, offer strong generalization but require extensive annotations. Interactiv…
Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis
Jiesi Hu, Yanwu Yang, Zhiyu Ye +4
The rise of In-Context Learning (ICL) for universal medical image segmentation has introduced an unprecedented demand for large-scale, diverse datasets for training, exacerbating t…
Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancement
Jiesi Hu, Jianfeng Cao, Yanwu Yang +4
In-context learning (ICL) offers a promising paradigm for universal medical image analysis, enabling models to perform diverse image processing tasks without retraining. However, c…
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…
DECOR-NET: A COVID-19 Lung Infection Segmentation Network Improved by Emphasizing Low-level Features and Decorrelating Features
Jiesi Hu, Yanwu Yang, Xutao Guo +1
Since 2019, coronavirus Disease 2019 (COVID-19) has been widely spread and posed a serious threat to public health. Chest Computed Tomography (CT) holds great potential for screeni…
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…