Excellence in Research: Harnessing Big Data and Domain Knowledge to Advance Deep Learning for Interpretable Cell Quantitation

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Accurate, reliable, and interpretable pancreatic cell recognition and evaluation allow better anticipation of changes in body response to disease or treatment. Thus, it is highly valuable to pathologists and scientists in clinical practice and laboratory work, who care for patients, especially for those with commodities like obesity or diabetes. This project designs and develops novel deep learning, transfer learning, and interpretable methods integrated with domain-expert knowledge for reliable, interpretable, and accurate pancreatic cell quantitation by harnessing and exploiting the abundance of big data. Existing methods do either manual recognition or require understanding of a huge number of parameters setting for automation. Additionally, these techniques do not impose interpretability and disregard domain-expert knowledge. The project tackles these issues by incorporating domain expert knowledge, transferring knowledge and experience from other image processing and segmentation problems adapting for cell quantitation. The project outcomes would benefit researchers in biomedical imaging, transfer learning, human-machine interaction in addition to providing practical studying materials in areas such as deep learning, image processing, and interpretable methods for students. Moreover, the project has the potential to increases research capacity and collaborations to generate new research opportunities for students from underrepresented communities to pursue advanced degrees in STEM.The project develops data-driven algorithms addressing interpretable cell quantitation problem. Facilitated by novel deep learning algorithms, the project harnesses the potential of big data to derive reliable, interpretable, and accurate evaluation. In addition, the project explores a generalized framework for extending existing findings and incorporating domain-expert knowledge to complement the modeling and learning process. Specifically, this research uses microscopy images, machine learning, transfer learning, and domain knowledge in three thrusts: (1) A deep learning based cell quantitation algorithm. The algorithm is featured with interpretability, guided by domain-expert knowledge for trust, reliable, and accurate performance. (2) Enhancing data-driven techniques in the area of transfer learning to fill important knowledge gaps at the intersection of the deep learning and biomedical imaging. (3) Advancing domain knowledge incorporation and human intervention methods for developing and operating deep learning based system for biomedical research and healthcare. All the research outcomes are made publicly available to facilitate extending and accelerating varied application development from diverse communities of researchers and students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.