Selected Publications

You can find my full publication list on my Google Scholar Profile or my ResearchGate Profile.
Remark: ^* Co-first authors, ^# Corresponding or Co-corresponding authors

• [CVPR 2024] FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning. (Project page)
  Junyuan Zhang^*, Shuang Zeng^*, Miao Zhang, Runxi Wang, Feifei Wang, Yuyin Zhou, Paul Pu Liang, Liangqiong Qu^#.
• [CVPR 2024] Residual Denoising Diffusion Models. (Paper, Code )
  Jiawei Liu, Qiang Wang, Huijie Fan^#, Yinong Wang, Yandong Tang, Liangqiong Qu^#.
• [CVPR 2024] Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding. (Paper, Code )
  Zhiheng Cheng, Qingyue Wei, Hongru Zhu, Yan Wang, Liangqiong Qu, Wei Shao, Yuyin Zhou.
• [MICCAI 2024] Tackling Data Heterogeneity in Federated Learning via Loss Decomposition.
  Shuang Zeng^*, Pengxin Guo^*, Shuai Wang, Jianbo Wang, Yuyin Zhou, and Liangqiong Qu^#.
• [Cancer Cell 2024] Advancing Presurgical Non-invasive Molecular Subgroup Prediction in Medulloblastoma Using Artificial Intelligence and MRI Signatures. (Paper, Code)
  Yan-Ran (Joyce) Wang, Pengcheng Wang, Zihan Yan, Quan Zhou, Fatma Gunturkun, Peng Li, Yanshen Hu, Wei Emma Wu, Kankan Zhao, Michael Zhang, Haoyi Lv, Lehao Fu, Jiajie Jin, Qing Du, Haoyu Wang, Kun Chen, Liangqiong Qu, Keldon Lin, Michael Iv, Hao Wang, Xiaoyan Sun, Hannes Vogel, Summer Han, Lu Tian, Feng Wu, and Jian Gong.
• [Radiology: AI 2023] AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans. (Paper)
  Yan-Ran (Joyce) Wang^*, Liangqiong Qu^*, Natasha Diba Sheybani, Xiaolong Luo, Jiangshan Wang, Kristina Elizabeth Hawk, Ashok Joseph Theruvath, Sergios Gatidis, Xuerong Xiao, Allison Pribnow, Daniel Rubin, Heike E. Daldrup-Link.
• [EMNLP 2023] Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation. (Paper) (Oral)
  Yanzhao Shi, Junzhong Ji, Xiaodan Zhang^#, Liangqiong Qu^#, and Ying Liu.
• [TMI 2023] Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging. (Paper, Code)
  Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou.
• [CHIL 2023] Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models. (Paper, Code) (Best Paper)
  Siyi Tang, Jared Dunnmon, Liangqiong Qu, Khaled Saab, Tina Baykaner, Christopher Lee-Messer, and Daniel Rubin.
• [IEEE JBHI 2022] Splitavg: A Heterogeneity-aware Federated Deep Learning Method for Medical Imaging. (Paper, Code)
  Miao Zhang^*, Liangqiong Qu ^#*, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin ^#.
• [IEEE CVPR 2022] Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning. (Paper, Code)
  Liangqiong Qu^*, Yuyin Zhou^*, Paul Pu Liang^*, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin.
• [PNAS 2022] High Precision Tumor Resection Down to Few-Cell Level Guided by NIR-IIb Molecular Fluorescence Imaging. (Paper)
  Feifei Wang^*, Liangqiong Qu^*, Ani Baghdasaryan^*, RuSiou Hsu, Peng Liang, Jiachen Li, Guanzhou Zhu, Zhuoran Ma, Hongjie Dai.
• [MeDIA 2020] Synthesized 7T MRI from 3T MRI via Deep Learning in Spatial and Wavelet Domains. (Paper, Code)
  Liangqiong Qu, Yongqin Zhang, Shuai Wang, Pew-Thian Yap, Dinggang Shen.
• [MICCAI 2019] Wavelet-based Semi-supervised Adversarial Learning for Synthesizing Realistic 7T from 3T MRI. (Paper) (Oral)
  Liangqiong Qu, Shuai Wang, Pew-Thian Yap, Dinggang Shen.
• [IEEE CVPR 2017] DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal. (Paper, Code, Training dataset, Test dataset) (Spotlight)
  Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, and Rynson W.H. Lau.