About Me

Hi, I am Yinghuan. I received my B.Sc. and Ph.D. degree both in Computer Science at Nanjing University in 2007 and 2013, respectively. Currently, I am an Associate Professor in the Department of Computer Science and Technology at Nanjing University, and a member of the National Key Laboratory for Novel Software Technology. Also, I am a member of the Reasoning and Learning Research Group, led by Prof. Yang Gao.

In 2011-2012, I visited the IDEA Lab of the University of North Carolina at Chapel Hill (UNC) under the support from the China Scholarship Council (CSC), and worked with Prof. Dinggang Shen. In 2016, I returned to the IDEA Lab for a three months visiting. Also, I spent two months in 2014 as a Visiting Scholar at the Data Science Lab of the University of Technology, Sydney (UTS), working with Prof. Longbing Cao.

Currently, I am broadly interested in medical image analysis, clinical data mining, and also including the other related topics in image processing, computer vision, and machine learning.

In particular, the main goal of our research is to develop the machine learning-based algorithms for the data analysis problems in medical imaging and clinical data. The difficulties in medical data, e.g., complicated connection, unpredictable noise, limited amount and insufficient labeling, pose the considerable challenges for the data analysis. We wish to develop the effective and efficient methods to tackle these challenges. Also, these methods are used to guide the development of the real systems in the clinical scenario.

Chinese Version (中文简介)

News

• [Feb, 2023] Three papers about DG Classification, Semi-supervised Learning and Barely-supervised Medical Image Segmentation have been accepted by CVPR 2023.

• [Sep, 2022] One paper about Barely Supervised Medical Image Segmentation has been accepted by TMI.

• [Sep, 2022] One paper about Barely-supervised Learning has been accepted by NeurIPS 2022.

• [Mar, 2022] 智能重症可解释评估模型 TSOFA 已上线（ A Time-Incorporated SOFA-Based Explainable Machine Learning Model for Mortality Prediction）与中国人民解放军东部战区总院重症医学科合作

• [Mar, 2022] Three papers about Semi-supervised Learning and DG Segmentation are accepted by CVPR 2022.

• [Feb, 2022] One paper about DG Classification is accepted by TCSVT 2022.

• [Dec, 2021] A paper about Self-training and Semi-supervised Learning is accepted as oral by AAAI 2022.

• [Nov, 2021] 入选中国人工智能学会—华为 MindSpore 学术奖励基金.

• [Oct, 2021] A talk at "Machine Learning Forum" of NCIIP 2021.

• [Oct, 2021] A talk at 2021 中国人工智能大会—人工智能核心技术攻坚青年科学家沙龙.

• [Oct, 2021] One paper about Semi-supervised Segmentation has been accepted by TMI.

• [Sep, 2021] An invited talk at CAAI-ML Western Forum.

• [Aug, 2021] One paper about DG Segmentation is accepted by PR.

• [Aug, 2021] One paper is accepted by NPJ Computational Materials.

• [Aug, 2021] One paper about UDA Medical Image Segmetation is accepted by TMI.

• [Jul, 2021] Two papers about Few-shot Segmentation and Style Transfer are accepted by ICCV 2021, both are orals.

• [Jul, 2021] An invited talk at "Forum of Young Scholars" of CCF-AI 2021.

• [Jul, 2021] A tutorial of "Effective Medical Image Analysis Models with Efficient Annotations" at ICME 2021.

Recent Publication

• Jintao Guo, Na Wang, Lei Qi, Yinghuan Shi. “ ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-Frequency Transform for Domain Generalization. ” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

• Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi. “ Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation. ” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

• Heng Cai, Shumeng Li, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao. “ Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation. ” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

• Yue Duan, Zhen Zhao, Lei Qi, Lei Wang, Luping Zhou, Yinghuan Shi, Yang Gao. “ MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization. ” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023. [Code]

• Shumeng Li, Heng Cai, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao. “ PLN: Parasitic-Like Network for Barely Supervised Medical Image Segmentation. ” IEEE Transactions on Medical Imaging (TMI), 2022. [Code]

• Guan Gui, Zhen Zhao, Lei Qi, Luping Zhou, Lei Wang, Yinghuan Shi. “ Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class. ” Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), 2022. (Accepted as Spotlight, 5%) [Code]

• Qian Yu, Lei Qi, Yang Gao, Wuzhang Wang, Yinghuan Shi. “ Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation. ” IEEE Transactions on Image Processing (TIP), 2022. [Code]

• Jian Zhang, Lei Qi, Yinghuan Shi, Yang Gao. “MVDG: A Unified Multi-view Framework for Domain Generalization.” European Conference on Computer Vision (ECCV), 2022. [Code]

• Ziqi Zhou, Lei Qi, Yinghuan Shi. “Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration.” European Conference on Computer Vision (ECCV), 2022. [Code]

• Yue Duan, Lei Qi, Lei Wang, Luping Zhou, and Yinghuan Shi. “RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning.” European Conference on Computer Vision (ECCV), 2022. [Code]

More Papers

Preprints

• Jintao Guo, Lei Qi, Yinghuan Shi, Yang Gao. “Domain Generalization via Progressive Layer-wise and Channel-wise Dropout.”  

More Preprints

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