About
I am a PhD student at The University of Hong Kong, supervised by Prof. Lequan Yu. I received my MPhil degree from Zhejiang University in 2023 and B.Eng degree from Harbin Institute of Technology in 2020.
My research lies at the intersection of artificial intelligence and medical image analysis, with a particular focus on computational pathology. I develop deep learning methods to enhance diagnostic capabilities and improve model generalizability.
I am also a Kaggle Competitions Master with three Gold and two Silver medals.
News
Our work on the generalization and fairness of pathology FMs has been accepted by Nature Communications!
Achieved a solo Gold Medal in Kaggle's "Drawing with LLMs" Competition!
Three papers were early accepted by MICCAI 2025, congrats to my co-authors!
Our paper on computational pathology has been accepted by IEEE Transactions on Medical Imaging (TMI)!
Our work on pathology foundation model adaptation has been accepted by NeurIPS 2024!
Our research on medical imaging has been accepted by Medical Image Analysis (MIA)!
Our study in neuropsychopharmacology has been accepted by Progress in Neuro-Psychopharmacology and Biological Psychiatry (PNP)!
Our work on continual learning for WSI analysis has been accepted by ICCV 2023!
Our research on brain age estimation has been accepted by Information Fusion (Impact Factor: 17.56)!
Achieved Gold Medal (Ranked 7th out of 1,165 teams) in Google's "Isolated Sign Language Recognition" Competition!
Our innovative approach in low-dose CT and low-dose PET image denoising has been accepted by Medical Image Analysis (MIA)!
Our research on gait recognition has been accepted by ICASSP 2023!
Secured Gold Medal (Ranked 4th out of 1,175 teams) in Kaggle's "Hacking the Human Body" Competition!
Selected Publications
View All →Knowledge-Guided Adaptation of Pathology Foundation Models Effectively Improves Cross-domain Generalization and Demographic Fairness
Yanyan Huang, Weiqin Zhao, Zhengyu Zhang, Yihang Chen, Yu Fu, Feng Wu, Yuming Jiang, Li Liang, Shujun Wang, Lequan Yu†
Nature Communications, 2025
This work presents a knowledge-guided adaptation framework that leverages task-specific information bottlenecks to disentangle robust pathological features from site-specific and demographic artifacts, achieving superior cross-domain generalization and fairness in computational pathology.
Bridging Radiological Images and Factors with Vision-Language Model for Accurate Diagnosis of Proliferative Hepatocellular Carcinoma
Yanyan Huang, Wanli Zhang, Peixiang Huang, Yu Fu, Ruimeng Yang, Lequan Yu†
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2025
This work introduces a vision-language foundation model-based approach that bridges radiological images and clinical factors through aligned embedding spaces, enabling effective multimodal fusion for accurate diagnosis of proliferative hepatocellular carcinoma.
Free lunch in pathology foundation model: Task-specific model adaptation with concept-guided feature enhancement
Yanyan Huang, Weiqin Zhao, Yihang Chen, Yu Fu, Lequan Yu†
Advances in Neural Information Processing Systems (NeurIPS), 2024
This work proposes a concept-guided feature enhancement paradigm that dynamically calibrates pathology foundation models through task-specific concept anchors, improving feature expressivity and discriminativeness for downstream clinical tasks while maintaining strong generalizability.
Unleash the Power of State Space Model for Whole Slide Image With Local Aware Scanning and Importance Resampling
Yanyan Huang, Weiqin Zhao, Yu Fu, Lingting Zhu, Lequan Yu†
IEEE Transactions on Medical Imaging (TMI), 2024
This work presents a state space model-based framework for whole slide image analysis that incorporates local-aware hierarchical scanning and test-time importance resampling to efficiently process gigapixel pathology images while maintaining high accuracy and robustness.
Conslide: Asynchronous hierarchical interaction transformer with breakup-reorganize rehearsal for continual whole slide image analysis
Yanyan Huang, Weiqin Zhao, Shujun Wang, Yu Fu, Yuming Jiang, Lequan Yu†
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023
This work introduces a continual learning framework for whole slide image analysis that employs hierarchical interaction transformers and breakup-reorganize rehearsal strategies to mitigate catastrophic forgetting while adapting to sequential datasets from evolving imaging technologies.