Huining Liang has worked on a new deep-learning Transformer-based segmentation technique. Since no one has applied it to stromule data before, this helps plant science researchers achieve a higher order, high throughput multiplexing. Huining has also applied Transformers for tracking. The overall impact includes the improvement over the previous pipeline, which involves UNet and traditional Computer vision-based methods. The current pipeline achieved higher accuracy than the earlier work.
Dr. Kambhamettu directs the Video/Image Modeling and Synthesis (VIMS) Lab, which has Ten PhD students working on the deep learning approaches. The approaches developed in this project open a way to incorporate some of the concepts in other projects.
Dr. Caplan directs a Bio-Imaging Center that is used by 19 different departments at the University of Delaware, spanning a wide array of disciplines. The approaches developed in this project can be translated to other projects in the network.
Nothing to report.
Yes, Huining Liang will use this training in her Ph.D. work and to assist others in research computing, therefore, adding to our human resource infrastructure.
Nothing to report.
In VIMS Lab, this project helps update the repository of techniques impacted by Huining’s work under CAREERS. It now includes transformer-based techniques as a contribution to this repository.
Nothing to report from PI and Mentor, but as far as Student Facilitator see notes taken during exit interview.
During Huining's exit interview she relayed the following that is very impactful about CAREERS and her experience:
(1) Because this was her second project, she felt she was given more responsibility to take the lead versus her first project which required more guidance and decision making for the overall project. This was confirmed by the PI and Mentor during their exit interviews as well that Huining learned so much after the first project in CAREERS, they felt she was capable of taking ownership and the lead as they felt comfortable she would ask them for help when needed.
(2) She appreciated the format change during the monthly meetings to require students to make an elevator pitch about their research/project in 2-3 mins before giving an update. She found this skill (elevator pitch) to be invaluable to think about explaining to your parents or anyone not in your field what you are doing, but it also allowed her to easily understand what other students were doing and to be able to find connections or possible networking opportunities in different domains using similar methodology/technology.
The project that Huining Liang worked on developed the use of a latest deep learning technique and compared against the previous pipeline, and achieved improved results. It will assist her future innovation in deep learning, and also will assist a Plant Genome Research Program looking at maize development for crop management. Thus, this project may potentially benefit crop production and food security which is a major societal impact.
* Explored Transformer based models for segmentation, detection and tracking on Microscopy Images.
* Improve and evaluate a working pipeline in research.
* Facilitate research with machine learning methods and HPC resources
* Trained the TransUNet model for segmentation of microscopy images.
* Compared with the previous UNet version in terms of computational cost, memory, and accuracy.
* Trained the TrackFormer for Stromule detection and tracking, and compared the tracking result with previous computer vision based approach.