Gan Lab
Principal Investigator: Assistant Professor Yu Gan
My research covers a wide spectrum of machine learning and deep learning techniques that facilitate biomedical imaging and dig out hidden information from medical/biomedical images.
I have been serving as PI for multiple research grants supported by National health institute (NIH), NJ Health Foundations, National Science Foundation (NSF), U.S. Department of Agriculture (USDA), and Burrough Wellcome foundation.
Research
My research agenda aims to develop cutting-edge medical data analytics and human computer interaction techniques to unlock the value of big medical image data, obtain new insights, generate actionable guidance, and facilitate clinical decision making.
Algorithm development
Super resolution and artifacts removal
We are developing a deep learning-based framework to enhance the optical and digital resolution of optical coherence tomography (OCT) systems. We develop a sparse representation-based method to removal saturation artifacts in OCT images.
Object detections
We developed a region proposal network to identify diseased regions in human coronaries and football players in sports data analysis.
Image segmentation
We developed a robust deep learning network to segment tissue regions within medical images.
Image de-noising
We developed a de-noising framework to de-noise MRI and ultrasound images.
Biomedical application
Cardiac characterization
Cervical collagen fiber image analysis and image informatics to better our understanding of preterm birth
Breast cancer identification for surgical margin detection
Research Position
We have Ph.D. position(s) opening in our lab for spring and fall enrollment. Please reach out Dr. Yu Gan ([email protected]) for more information.