Field of Research (FoR)
Dr Yang Song is an ARC Future Fellow, Scientia Fellow and Senior Lecturer in the School of Computer Science and Engineering. Her research area is in Computer Vision, Biomedical Image Analysis, Machine Learning, Deep Learning, and General AI. She has over 100 publications including papers in TMI, MedIA, NeuroImage, BMC Bioinformatics, CVPR, ICCV, AAAI, IJCAI, and MICCAI. Her current research mainly focuses on the development of machine learning...view more
Dr Yang Song is an ARC Future Fellow, Scientia Fellow and Senior Lecturer in the School of Computer Science and Engineering. Her research area is in Computer Vision, Biomedical Image Analysis, Machine Learning, Deep Learning, and General AI. She has over 100 publications including papers in TMI, MedIA, NeuroImage, BMC Bioinformatics, CVPR, ICCV, AAAI, IJCAI, and MICCAI. Her current research mainly focuses on the development of machine learning and deep learning algorithms for computer vision problems such as:
- Abnormality detection and segmentation
- Morphological analysis in microscopy images
- Cell segmentation and tracking
- Biomarker detection and analysis
- General image classification
- Object detection and recognition
- 3D image reconstruction
- Action recognition and video analysis
Personal website - http://www.cse.unsw.edu.au/~ysong/
Actively recruiting PhD students. Scholarships available.
- 2020 Scientia Fellowship, UNSW
- 2019 ARC Future Fellowship
- 2017 Dean’s Research Award, University of Sydney
- 2015 ARC Discovery Early Career Researcher Award (DECRA)
- 2013 Google Publication Prize
My Research Supervision
Areas of supervision
Key areas: biomedical image analysis, general computer vision, deep learning, machine learning, interdisciplinary computer vision applications, multimedia data analysis
Currently supervising multiple PhD students in microscopy and MRI image analysis, and general computer vision problems
** Multiple PhD scholarships available**, with internship opportunities at leading US institutes. Interested candidates are strongly encouraged to apply.
Example topics include:
Morphology analysis in microscopy images
Various types of microscopy images are widely used in biological research to aid our understanding of human biology. Cellular and molecular morphologies give lots of information about the underlying biological processes. The ability to identify and describe the morphological information quantitative, objectively, and efficiently is critical. In this PhD project, we will investigate various computer vision, machine learning (especially deep learning), and statistical analysis methodologies to develop automated morphology analysis methods for microscopy images.
Cell segmentation and event detection in microscopy images
One type of morphology analysis approach focuses on the cell-level information extraction from microscopy images. Normal cells, cancer cells, and special events (such as mitosis) are the important objects of interest. Detection and segmentation of these cells are challenging with the large variety of tissues and inconsistent imaging conditions. In this project, we will investigate various segmentation algorithms with a particular focus on machine/deep learning.
Lesion detection and segmentation in MRI
Detection and segmentation of lesions in MRI are routinely performed in radiology centres for patient diagnosis and treatment planning. This is a time-consuming process and prone to inter-observer variability. Computerized methods have been developed over the years but there is still much scope of improvement. In this PhD project, we will investigate various types of lesions, such as brain tumours, with advanced deep learning algorithms and other techniques such as deformable models.
Object detection and fine-grained recognition
This is an emerging topic in general computer vision. The objective is to detect the object (e.g. a cat) and classify the object at a fine-grained level (e.g. a siamese cat), and such systems would have a large variety of practical applications in real life scenarios. Recently lots of research progress have been reported in this area. In this PhD project, we will investigate the advanced methodologies, which mainly involve various types of deep learning models, with a particular focus to address the challenges associated with insufficient sample sizes.
Weakly supervised object localization
With limited image-level annotations, a weakly-supervised approach is essential to localize the objects of interest in images. Such a method helps to reduce the amount of detailed ground truth annotation, which is traditionally necessary for fully-supervised machine or deep learning methods. In this PhD project, we will investigate the advanced methodologies, which mainly involve various types of deep learning models, with a wide variety of application areas in both general and biomedical image domains.
- COMP9517 - Computer Vision
- COMP9417 - Machine Learning and Data Mining
- Supervision of Honours thesis studies and Master's research project studies