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Yang Song

ARC Future Fellow, UNSW Scientia Fellow, Senior Lecturer
Research Area: AI and Computer Vision

School of Computer Science and Engineering
University of New South Wales

Office: Room 401E, Building K17
Email: yang.song1 AT unsw.edu.au
Google Scholar        UNSW Researcher        DBLP

About Me

I am an ARC Future Fellow, UNSW Scientia Fellow and Senior Lecturer (equivalent to Associate Professor in the U.S.) at the School of Computer Science and Engineering, Faculty of Engineering, UNSW Sydney, Australia. I graduated with a BEng (Honours 1) in Computer Engineering from Nanyang Technological University, Singapore, and obtained a PhD degree in Computer Science (medical image analysis) from the University of Sydney in 2013.

I received the highly competitive Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) in 2015, and was an ARC DECRA Fellow at the University of Sydney before joining UNSW as a Lecturer in 2018. I also received a Dean's Research Award from the Faculty of Engineering, University of Sydney in 2017. In 2019, I was awarded the prestigious ARC Future Fellowship, which provides support for excellent mid-career researchers to undertake high quality research in areas of national and international benefit. In 2020, I was awarded a Scientia Fellowship from UNSW, which supports career development of outstanding researchers. In 2021, I received two grant awards from Google and the Faculty of Engineering Research Excellence Award.

News (2022)

  • 11/2022: One paper accepted in Neural Networks.

  • 10/2022: I am listed among the World Top 2% Scientists in the Stanford 2022 list published at PLOS/Elsevier.

  • 09/2022: I have been promoted to Associate Professor, effective Jan 2023.

  • 08/2022: Received the Certificate of IEEE TMI Silver Distinguished Reviewer.

  • 08/2022: One paper accepted in Medical Image Analysis.

  • 08/2022: One paper accepted in NeuroImage.

  • 07/2022: I have joined the editorial board of IEEE TMI as an Associate Editor.

  • 06/2022: Three papers accepted in MICCAI 2022.

  • 05/2022: I am organising a Tutorial session at MICCAI 2022.

  • 04/2022: I am editing a Research Topic in Frontiers in Radiology.

  • 03/2022: One paper accepted in Medical Image Analysis.

  • 03/2022: Two papers accepted in CVPR 2022.

  • 03/2022: One paper accepted in IEEE Transactions on Medical Imaging.

  • 03/2022: I am serving as an Area Chair for MICCAI 2022.

  • 02/2022: I am selected as a Research Innovator in a Google Cloud program.

  • 02/2022: One paper accepted in IEEE Journal of Biomedical and Health Informatics.

  • 01/2022: One paper accepted in IEEE Transactions on Multimedia.

  • 01/2022: Six papers accepted in ISBI 2022.

Research Interests

My research focuses on developing Computer Vision, Machine and Deep Learning and, more generally, Human-centred AI methodologies for biomedical image analysis and other applications for social good.

Some of the specific research problems include:

  • Segmentation in radiological images

  • Cancer analysis in histopathology images

  • Cell segmentation and tracking

  • Point clouds analysis

  • 3D image reconstruction

  • Object detection and recognition

  • Action recognition and video analysis

  • Vision-based autonomous driving

  • Image enhancement and translation

  • Graph data modelling and analysis

I have produced over 150 peer-reviewed publications including papers in TMI, MedIA, TIP, NeuroImage, BMC Bioinformatics, CVPR, ICCV, AAAI, IJCAI, ACMMM and MICCAI. A full list of my publications can be seen from my Google Scholar.

I am an Associated Editor for IEEE Transactions on Medical Imaging. I am also an Area Chair for MICCAI 2022 and was a Senior Program Committee member for AAAI 2021-22 and IJCAI 2021; Program Committee member for IJCAI-PRICAI 2020, PAKDD 2018-20 and CVPR Workshop on CVMI 2019-21; and Associate Editor and Session Co-chair ISBI 2021. I am a regular reviewer for IEEE TPAMI, TMI and TIP, Nature Communications, CVPR, ECCV, MICCAI, WACV, KDD, BMVC and ISBI.

PhD applications: You can choose one of the research topics listed above, or propose a topic that is aligned with my research interests in computer vision, biomedical image analysis and machine/deep learning.
I also have a strong interest in integrating knowledge representation with learning and more general questions around AI, with a list of research topics listed on Prof Maurice Pagnucco's site.
Interested candidates with relevant academic background (Honours 1 equivalent) are strongly encouraged to apply. Please send me an email including your CV and transcripts. Scholarship information can be found here.

Research Themes

Computational histopathology:
Computational histopathology aims to discover phenotypic information for cancer diagnosis and prognosis from histopathology images. This field targets a variety of fundamental computer vision problems, such as cell nuclei segmentation, tumour detection, cancer classification, biomarker analysis and survival prediction. Some of the selected publications include:

  • R. Guo, M. Pagnucco, Y. Song. Learning with noise: Mask-guided attention model for weakly supervised nuclei segmentation. MICCAI, 2021. [Code]

  • C. Cong, S. Liu, A. Di Ieva, M. Pagnucco, S. Berkovsky, Y. Song. Semi-supervised adversarial learning for stain normalisation in histopathology images. MICCAI, 2021.

  • L. Fan, A. Sowmya, E. Meijering, Y. Song. Learning visual features by colorization for slide-consistent survival prediction from whole slide images. MICCAI, 2021.

  • X. Wang, T. Xiang, C. Zhang, Y. Song, D. Liu, H. Huang, W. Cai. EX-NAS: Searching efficient bi-directional architecture for medical image segmentation. MICCAI, 2021. [Code]

  • D. Liu, D. Zhang, Y. Song, H. Huang, W. Cai. Panoptic feature fusion net: A novel instance segmentation paradigm for biomedical and biological images. IEEE Transactions on Image Processing, 2021. [Code]

  • D. Liu, D. Zhang, Y. Song, F. Zhang, L. O'Donnell, H. Huang, M. Chen, W. Cai. PDAM: A panoptic-level feature alignment framework for unsupervised domain adaptive instance segmentation in microscopy images. IEEE Transactions on Medical Imaging, 2020. [Code]

  • T. Xiang, C. Zhang, D. Liu, Y. Song, H. Huang, W. Cai. BiO-Net: Learning recurrent bio-directional connections for encoder-decoder architecture. MICCAI, 2020. [Code]

  • D. Liu, D. Zhang, Y. Song, F. Zhang, L. O'Donnell, H. Huang, M. Chen, W. Cai. Unsupervised instance segmentation in microscopy images via panoptic domain adaptation and task re-weighting. CVPR, 2020. [Code]

  • D. Liu, D. Zhang, Y. Song, C. Zhang, F. Zhang, L. O'Donnell, W. Cai. Nuclei segmentation via a deep panoptic model with semantic feature fusion. IJCAI, 2019. [Code]

  • D. Zhang, Y. Song, D. Liu, H. Jia, S. Liu, Y. Xia, H. Huang, W. Cai. Panoptic segmentation with an end-to-end cell R-CNN for pathology image analysis. MICCAI, 2018.

Segmentation in medical images:
In many medical image analysis tasks, segmentation is the essential step to enable downstream analysis related to medical diagnosis and treatment planning. We have investigated various problems in this field, such as vessel segmentation in optical images, prostate and muscle segmentation in MR images. Some of the selected publications include:

  • J. Zhu, B. Bolsterlee, B. Chow, C. Cai, R. Herbert, Y. Song, E. Meijering. Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy. NMR in Biomedicine, 2021.

  • Y. Wu, Y. Xia, Y. Song, Y. Zhang, W. Cai. NFN+: A novel network followed network for retinal vessel segmentation. Neural Networks, 2020.

  • H. Jia, Y. Song, H. Huang, W. Cai, Y. Xia. HD-Net: Hybrid discriminative network for prostate segmentation in MR images. MICCAI, 2019.

  • Y. Wu, Y. Xia, Y. Song, D. Zhang, D. Liu, C. Zhang, W. Cai. Vessel-Net: Retinal vessel segmentation under multi-path supervision. MICCAI, 2019.

  • H. Jia, Y. Xia, Y. Song, D. Zhang, H. Huang, Y. Zhang, W. Cai. 3D APA-Net: 3D adversarial pyramid anisotropic convolutional network for prostate segmentation in MR images. IEEE Transactions on Medical Imaging, 2019.

  • Y. Wu, Y. Xia, Y. Song, Y. Zhang, W. Cai. Multiscale network followed network model for retinal vessel segmentation. MICCAI, 2018.

Computational neuroscience:
Computational neuroscience helps provide a mechanism to understand human brain's functionalities and gain insights into the cause and progression of neurological diseases. Our research is focused on single neuron reconstruction from light microscopy images, brain tumour and lesion segmentation in MRI and tractography analysis in DTI. Some of the selected publications include:

  • Y. Chen, C. Zhang, Y. Song, N. Makris, Y. Rathi, W. Cai, F. Zhang, L. O'Donnell. Deep fiber clustering: Anatomically informed unsupervised deep learning for fast and effective white matter prediction. MICCAI, 2021.

  • H. Wang, D. Zhang, Y. Song, S. Liu, D. Feng, Y. Wang, H. Peng, W. Cai. Segmenting neuronal structure in 3D optical microscopy images via knowledge distillation with teacher-student network. ISBI, 2019.

  • H. Wang, D. Zhang, Y. Song, S. Liu, H. Huang, M. Chen, H. Peng, W. Cai. Multiscale kernel for enhanced U-shaped network to improve 3D neural tracing. CVPRW, 2019.

  • S. Liu, D. Zhang, Y. Song, H. Peng, W. Cai. Automated 3-D neuron tracing with precise branch erasing and confidence controlled back tracking. IEEE Transactions on Medical Imaging, 2018. [Code]

  • F. Zhang, W. Wu, L. Ning, G. McAnulty, D. Weber, B. Gagoski, K. Sarill, H. Hamoda, Y. Song, W. Cai, Y. Rathi, L. O'Donnell. Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis. NeuroImage, 2018.

  • D. Zhang, S. Liu, Y. Song, D. Feng, H. Peng, W. Cai. Automated 3D soma segmentation with morphological surface evolution for neuron reconstruction. Neuroinformatics, 2018.

  • D. Liu, D. Zhang, Y. Song, F. Zhang, L. O'Donnell, W. Cai. 3D large kernel anisotropic network for brain tumor segmentation. ICONIP, 2018.

  • F. Zhang, P. Savadjiev, W. Cai, Y. Song, Y. Rathi, B. Tunc, D. Parker, T. Kapur, R. Schultz, N. Makris, R. Verma, L. O'Donnell. Whole brain white matter connectivity analysis using machine learning: An application to autism. NeuroImage, 2017.

Pattern analysis in biomedical images:
Pattern analysis is another fundamental problem in biomedical imaging and many computer-aided diagnosis and biological discovery tasks can be formulated into such problems. We conduct studies on a variety of problem domains and imaging modalities such as X-Ray, CT, light microscopy and histopathologies. Some of the selected publications include:

  • P. Rana, A. Sowmya, E. Meijering, Y. Song. Estimation of three-dimensional chromatin morphology for nuclear classification and characterisation. Scientific Reports, 2021.

  • Y. Xie, Y. Xia, J. Zhang, Y. Song, D. Feng, M. Fulham, W. Cai. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Transactions on Medical Imaging, 2018.

  • Y. Song, Q. Li, H. Huang, D. Feng, M. Chen, W. Cai. Low dimensional representation of Fisher vectors for microscopy image classification. IEEE Transactions on Medical Imaging, 2017.

  • Y. Song, Q. Li, F. Zhang, H. Huang, D. Feng, Y. Wang, M. Chen, W. Cai. Dual discriminative local coding for tissue aging analysis. Medical Image Analysis, 2017.

  • Y. Song, W. Cai, H. Huang, D. Feng, Y. Wang, M. Chen. Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors. BMC Bioinformatics, 2016.

  • F. Zhang, Y. Song, W. Cai, S. Liu, S. Liu, S. Pujol, R. Kikinis, Y. Xia, M. Fulham, D. Feng, ADNI. Pairwise latent semantic association for similarity computation in medical imaging. IEEE Transactions on Biomedical Engineering, 2016.

  • F. Zhang, Y. Song, W. Cai, A. Hauptmann, S. Liu, S. Pujol, R. Kikinis, M. Fulham, D. Feng, M. Chen. Dictionary pruning with visual word significance for medical image retrieval. Neurocomputing, 2016.

  • Y. Song, W. Cai, H. Huang, Y. Zhou, D. Feng, Y. Wang, M. Fulham, M. Chen. Large margin local estimate with applications to medical image classification. IEEE Transactions on Medical Imaging, 2015.

  • Y. Song, W. Cai, H. Huang, Y. Zhou, Y. Wang, D. Feng. Locality-constrained subcluster representation ensemble for lung image classification. Medical Image Analysis, 2015.

General image, video and point clouds analysis:
Driven by interests in solving underlying challenges in feature representation and deep learning, we also work on general computer vision problems, such as texture classification, action recognition, video captioning, pedestrian trajectory prediction, point clouds analysis and image restoration. Some of the selected publications include:

  • L. Li, M. Pagnucco, Y. Song. Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction. CVPR, 2022. [Code]

  • Y. Bai, J. Wang, Y. Long, B. Hu, Y. Song, M. Pagnucco, Y. Guan. Discriminative latent semantic graph for video captioning. ACM MM, 2021. [Code]

  • J. Wang, Y. Long, M. Pagnucco, Y. Song. Dynamic graph warping transformer for video alignment. BMVC, 2021.

  • T. Xiang, C. Zhang, Y. Song, J. Yu, W. Cai. Walk in the cloud: Learning curves for point clouds shape analysis. ICCV, 2021. [Code]

  • C. Zhang, J. Yu, Y. Song, W. Cai. Exploiting edge-oriented reasoning for 3D point-based scene graph analysis. CVPR, 2021. [Code]

  • H. Lin, M. Pagnucco, Y. Song. Edge guided progressively generative image outpainting. CVPRW, 2021.

  • J. Yu, C. Zhang, Y. Song, W. Cai. ICE-GAN: Identify-aware and capsule-enhanced GAN with graph-based reasoning for micro-expression recognition and synthesis. IJCNN, 2021. [Code]

  • C. Zhang, Y. Song, L. Yao, W. Cai. Shape-oriented convolutional neural network for point cloud analysis. AAAI, 2020.

  • V. Dodballapur, R. Calisa, Y. Song, W. Cai. Automatic dropout for deep neural networks. ICONIP, 2020.

  • Y. Song, F. Zhang, Q. Li, H. Huang, L. O'Donnell, W. Cai. Locally-transferred Fisher vectors for texture classification. ICCV, 2017.

  • Y. Song, W. Cai, Q. Li, F. Zhang, D. Feng, H. Huang. Fusing subcategory probabilities for texture classification. CVPR, 2015.

Machine ethics:
Machine ethics is about implementing AI programs that enable moral and ethical behaviours in cognitive machines. We focus on developing logical reasoning based explicit approaches to machine ethics. We have recently organised the First International Workshop on Computational Machine Ethics (CME 2021) in conjunction with KR 2021. Some of the selected publications include:

  • M. Pagnucco, D. Rajaratnam, R. Limarga, A. Nayak, Y. Song. Epistemic reasoning for machine ethics with situation calculus. AIES, 2021.

  • R. Limarga, M. Pagnucco, Y. Song, A. Nayak. Non-monotonic reasoning for machine ethics with situation calculus. AI, 2020.

Research Group

Current PhD Students (as primary or joint supervisor)
Priyanka Rana (2019.02 - )
Chaoyi Zhang (2019.02 - )
Yuqian Chen (2019.08 - )
Ari Tchetchenian (2020.02 - )
Jiayi Zhu (2020.06 - )
Cong Cong (2020.06 - )
Kunzi Xie (2020.06 - )
Heng Wang (2020.07 - )
Junyan Wang (2020.08 - )
Raynaldio Limarga (2020.09 - )
Lei Fan (2020.09 - )
Yan Hu (2020.10 - )
Piumi Don Simonge (2020.10 - )
Renhao Huang (2021.02 - )
Arnisha Khondaker (2021.03 - )
Zhan Heng (2021.06 - )
Tammy Zhong (2022.02 - )
Shizuka Hayashi (2022.02 -)
Shenghui Yan (2022.02 -)
Wenbin Wang (2022.05 -)
Marium Malik (2022.05 -)
Ruoyu Guo (2022.05 -)
Yongze Wang (2022.05 -)

Current MPhil Students (as primary or joint supervisor)
Peibo Li (2020.09 - )
Yu Liu (2020.09 - )
Yiwen Xu (2021.03 - )

Alumni
Yixing Yang (Post-doc, 2021.06 - 2022.05)
Henry Liang (Software engineer, 2021.09 - 2021.12)

Graduated PhD Students (as joint supervisor)
Fan Zhang (now at Harvard University)
Afaf Tareef (now at Mutah University)
Siqi Liu (now at Siemens Healthineers, US)
Donghao Zhang (now at Monash University)
Dongnan Liu (now at University of Sydney)

Visiting PhD Students (graduated)
Haozhe Jia (from Northwestern Polytechnical University)
Yicheng Wu (from Northwestern Polytechnical University)

Teaching and Projects

  • COMP9517: Computer Vision

  • COMP9417: Machine Learning and Data Mining

  • COMP9491: Applied Artificial Intelligence

  • Honours thesis and MIT research projects supervision - interested students please email for topic discussion

  • Vertically Integrated Project (VIP): AI-4-Everyone