Guoqing Bao

Guoqing Bao

Senior staff researcher at Enflame Technology, obtained PhD in Artificial Intelligence & Image Processing from School of Computer Science, University of Sydney. Currently doing research in AI framework optimization & computer vision. Before his oversea study in Australia, he had worked as a senior software engineer in Beijing (e.g., iQiYi.com) from 2008 to 2016.

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portfolio

publications

3D Segmentation of Residual Thyroid Tissue Using Constrained Region Growing and Voting Strategies

Published in International Conference on Digital Image Computing: Techniques and Applications, 2017

We proposed a voting based region growing method for image segmentation

Recommended citation: Guoqing Bao, Chaojie Zheng, Panli Li, Hui Cui, Xiuying Wang, Shaoli Song, Gang Huang, Dagan Feng (2017). "3D Segmentation of Residual Thyroid Tissue Using Constrained Region Growing and Voting Strategies" International Conference on Digital Image Computing: Techniques and Applications pp. 1-5, doi: 10.1109/DICTA.2017.8227384 https://ieeexplore.ieee.org/document/8227384

Identification of lncRNA Signature Associated With Pan-cancer Prognosis

Published in IEEE Journal of Biomedical and Health Informatics, 2020

We proposed a machine learning based framework for pan-cancer genomic analysis. Code: https://github.com/guoqingbao/PanCancerLncRNA

Recommended citation: Guoqing Bao, Ran Xu, Xiuying Wang, Jianxiong Ji, Linlin Wang, Wenjie Li, Qing Zhang, Bin Huang, Anjing Chen, Beihua Kong, Qifeng Yang, Xinyu Wang, Jian Wang, Xingang Li. (2020). "Identification of lncRNA Signature Associated With Pan-cancer Prognosis" IEEE Journal of Biomedical and Health Informatics. doi: 10.1109/JBHI.2020.3027680. https://doi.org/10.1109/JBHI.2020.3027680

Depthwise Multiception Convolution for Reducing Network Parameters without Sacrificing Accuracy

Published in The 16th International Conference on Control, Automation, Robotics and Vision, 2020

We proposed a new convolutional method to improve the performance of depthwise separable convolution Code:https://github.com/guoqingbao/Multiception

Recommended citation: Guoqing Bao, Manuel B. Graeber, Xiuying Wang (2020). "Depthwise Multiception Convolution for Reducing Network Parameters without Sacrificing Accuracy" International Conference on Control, Automation, Robotics and Vision pp. 747-752, doi: 10.1109/ICARCV50220.2020.9305369 https://doi.org/10.1109/ICARCV50220.2020.9305369

A Bifocal Classification and Fusion Network for Multimodal Image Analysis in Histopathology

Published in The 16th International Conference on Control, Automation, Robotics and Vision, 2020

We proposed a bifocal classification and fusion network for analysis of pathology images

Recommended citation: Guoqing Bao, Manuel B. Graeber, Xiuying Wang (2020). "A Bifocal Classification and Fusion Network for Multimodal Image Analysis in Histopathology" International Conference on Control, Automation, Robotics and Vision pp. 747-752, doi: 10.1109/ICARCV50220.2020.9305360 https://doi.org/10.1109/ICARCV50220.2020.9305360

PathoFusion: An open-source AI framework for recognition of pathomorphological features and mapping of immunohistochemical data

Published in Cancers, 2021

We proposed an AI framework for cross-modality analysis of whole-slide pathology images. Code: https://github.com/guoqingbao/Pathofusion.

Recommended citation: Guoqing Bao, Xiuying Wang, Ran Xu, Christina Loh, Oreoluwa Daniel Adeyinka, Dula Asheka Pieris, Svetlana Cherepanoff, Gary Gracie, Maggie Lee, Kerrie L. McDonald, Anna K. Nowak, Richard Banati, Michael E. Buckland, and Manuel B. Graeber, (2021). "PathoFusion: An open-source AI framework for recognition of pathomorphological features and mapping of immunohistochemical data" Cancers. 13(4):617. https://doi.org/10.3390/cancers13040617

Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects

Published in Lancet EBiomedicine, 2021

We proposed a deep learning based framework for evaluation of metabolic disorders and surgery-induced weight loss effects using CT texture features extracted from human CT visceral. Code: https://github.com/guoqingbao/DeepAdipose.

Recommended citation: Juan Shi and Guoqing Bao (co-first author) et al., (2021). "Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects" Lancet EBiomedicine, vol. 69, p. 103471, 2021, doi: 10.1016/j.ebiom.2021.103471. https://doi.org/10.1016/j.ebiom.2021.103471

COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for COVID-19 Diagnosis and Severity Assessment

Published in Pattern Recognition, 2021

We proposed a multitask learning framework for COVID-19 diagnosis and serverity assessment. Code: https://github.com/guoqingbao/COVID-MTL.

Recommended citation: Guoqing Bao et al., (2021). "COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for COVID-19 Diagnosis and Severity Assessment" Pattern Recognition, 2021, doi: 10.1016/j.patcog.2021.108499. https://doi.org/10.1016/j.patcog.2021.108499

An Open-Source AI Framework for the Analysis of Single Cells in Whole-Slide Images with a Note on CD276 in Glioblastoma

Published in Cancers, 2022

We extended the Pathofusion framework for cell-level profiling on histopathology images.

Recommended citation: Alzoubi Islam, Guoqing Bao, Rong Zhang, Christina Loh, Yuqi Zheng, Svetlana Cherepanoff, Gary Gracie, Maggie Lee, Michael Kuligowski, Kimberley L. Alexander, Michael E. Buckland, Xiuying Wang, and Manuel B. Graeber, 2022, "An Open-Source AI Framework for the Analysis of Single Cells in Whole-Slide Images with a Note on CD276 in Glioblastoma", Cancers, no. 14: 3441, doi: 10.3390/cancers14143441. https://doi.org/10.3390/cancers14143441

Multi-task deep learning for medical image computing and analysis: A review

Published in Computers in Biology and Medicine, 2023

This review focuses on the advanced applications of multitask learning for medical image computing and analysis.

Recommended citation: Yan Zhao, Xiuying Wang, Tongtong Che, Guoqing Bao, and Shuyu Li. 2023. Multi-task deep learning for medical image computing and analysis: A review. Comput. Biol. Med. 153, C (Feb 2023). https://doi.org/10.1016/j.compbiomed.2022.106496 https://dl.acm.org/doi/abs/10.1016/j.compbiomed.2022.106496

PresCount: Effective Register Allocation for Bank Conflict Reduction

Published in IEEE/ACM CGO, 2024

Addressing the challenges of bank conflicts in register allocation in AI computing hardware.

Recommended citation: X. Guan, H. Zhou, G. Bao, H. Li, L. Zhu and J. Yao, "PresCount: Effective Register Allocation for Bank Conflict Reduction," 2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), Edinburgh, United Kingdom, 2024, pp. 170-181, doi: 10.1109/CGO57630.2024.10444841. https://ieeexplore.ieee.org/abstract/document/10444841

SPHINX: Search Space-Pruning Heterogeneous Task Scheduling for Deep Neural Networks

Published in ACM ICPP 2024, 2024

An efficient and novel search space-pruning heterogeneous task scheduling engine to improve the DNN execution performance.

Recommended citation: Bowen Yuchi, Heng Shi, and Guoqing Bao, "SPHINX: Search Space-Pruning Heterogeneous Task Scheduling for Deep Neural Networks", In Proceedings of the 53rd International Conference on Parallel Processing. Association for Computing Machinery, New York, NY, USA, 524-533. https://doi.org/10.1145/3673038.3673155 https://doi.org/10.1145/3673038.3673155

UFront: Toward A Unified MLIR Frontend for Deep Learning.

Published in IEEE/ACM ASE 2024, 2024

A unified frontend for the MLIR ecosystem which is able to transform popular models written in different frameworks to standard MLIR dialect.

Recommended citation: Guoqing Bao, Heng Shi, Chengyi Cui, Yalin Zhang, and Jianguo Yao, "UFront: Toward A Unified MLIR Frontend for Deep Learning.", In 39th IEEE/ACM International Conference on Automated Software Engineering (ASE’24), October 27-November 1, 2024, Sacramento, CA, USA. https://doi.org/10.1145/3691620.3695002 https://doi.org/10.1145/3691620.3695002

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