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Education
2018 - 2023: Ph.D. in Computer Science, HIT, China.
2016 - 2018: M.Sc. in Optical enginering, HIT, China.
2010 - 2014: B.Sc. in Optical and Electrical enginering , CUST, China.
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News
09/2023: Wow! Two MedIA paper got accepted in two months! Our "Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning" is accepted by MedIA
08/2023: Our paper, "Curriculum label distribution learning for imbalanced medical image segmentation" is accepted by MedIA
01/2023: We have relased the MICCAI INSTANCE 2022 challenge summarized paper on arxiv, "The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge" arxiv
09/2022: The INSTANCE 2022 challenge has successfully finished! Ten out of 74 teams won the prizes. We have released the challenge results on the test set Results.
06/2022: Our paper "ULTRA: Uncertainty-Aware Label Distribution Learning for Breast Tumor Cellularity Assessment" is accepted by MICCAI 2022
04/2022: The training data of the INSTANCE 2022 challenge has beed released!
03/2022: We will host an Intracranial Hemorrhage segmentation challenge INSTANCE 2022 at MICCAI2022. Welcome to participate in it.
08/2021: Our paper "Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation" is accepted by IEEE-JBHI
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Research
I do research on medical image analysis and computer vision, with a focus on learning with imperfect labels. Recently, I am also working on topics including prompt leaning in medical image analysis and diffusion models.
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Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning
Xiangyu Li, Xinjie Liang, Gongning Luo, Wei Wang, Kuanquan Wang, Shuo Li
Medical image analysis (MedIA),2023
Paper| Code
The first work that simultaneously model inter-rater and intra-rater ambiguity of tumor cellularity(TC) estimation tasks.
The proposed method significantly improves both segmentation-based and regression-based methods on the TC estimation task.
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Curriculum label distribution learning for imbalanced medical image segmentation
Xiangyu Li, Gongning Luo, Wei Wang, Kuanquan Wang, Shuo Li
Medical image analysis (MedIA),2023
Paper| Code
The first work to solve the label distribution imbalance problem in LDL-based segmentation tasks.
The proposed CLDL effectively combines curriculum learning and deep label distribution learning in a unified framework.
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ULTRA: Uncertainty-Aware Label Distribution Learning for Breast Tumor Cellularity Assessment
Xiangyu Li, Xinjie Liang, Gongning Luo, Wei Wang, Kuanquan Wang, Shuo Li
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Paper| Code
The first work to model label ambiguity of tumor cellularity(TC) by transfferring the TC score regression to a label distribution learning problem.
The proposed method significantly improves both segmentation-based and regression-based methods on the TC estimation task.
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Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation
Xiangyu Li, Xinjie Liang, Gongning Luo, Wei Wang, Kuanquan Wang, Shuo Li
IEEE Journal of Biomedical and Health Informatics (J-BHI)
Paper| Code
A new insight for intracranial hemorrhage segmentation by incorporating the hematoma expansion
into the segmentation network.
A novel way to exploit context information in intracranial hemorrhage segmentation by directly modeling the hematoma variation between two adjacent slices.
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