KA$^2$ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation
Shangde Gao
Ph.D. Candidate · ZJU RealLab & Liangzhu Lab · Center for Data Science · College of Computer Science and Technology
Github: ShangdeGao · Scholar: Google Scholar
Email: gaosde [at] zju [dot] edu [dot] cn · Hangzhou, Zhejiang, China · 310058
About
I am a final-year Ph.D. candidate at the College of Computer Science and Technology, Zhejiang University, supervised by Prof. Jian Wu. I am scheduled to graduate in Sep 2025.
I obtained my M.S. in Computer Science from Hunan University (July 2020), advised by Prof. Xin Liao at the College of Computer Science and Electronic Engineering. I received my B.S. in Software Engineering from Hunan University in July 2017.
My research focuses on Computer Vision and AI for Science, including knowledge distillation and fusion for real-world classification and segmentation tasks.
Latest news
One paper was accepted by NeurIPS 2025.
Two papers were accepted by MICCAI 2025.
One paper was accepted by the International Joint Conference on Artificial Intelligence (IJCAI).
One paper was accepted by the International Conference on Intelligent Computing (ICIC).
One paper was accepted by the International Joint Conference on Neural Networks (IJCNN).
One paper was accepted by the MICCAI 2024 CARE challenge.
One paper was accepted by Information Sciences (IF=8.1).
One paper was accepted by Neural Computing and Applications (IF=4.7).
One paper was accepted by ICANN.
One paper was accepted by IJCNN.
Selected publications
Collaborative knowledge amalgamation: Preserving discriminability and transferability in unsupervised learning
Contrastive Knowledge Amalgamation for Unsupervised Image Classification
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