Ji-Xin Cheng

Automating and Integrating Additive Manufacturing of Nanoengineered Materials

PROJECT DESCRIPTION
Breast cancer is the most common cancer among women worldwide. The detection and treatment of breast cancer largely rely on the histology of breast tissue. Staining methods are routinely used for tissue histology, however, the staining process is time-consuming, labor-intensive, sometimes hard to repeat, and has low chemical specificity. Stimulated Raman scattering (SRS) is a label-free chemical imaging method based on intrinsic molecule vibration, which is promising to overcome the limitation of traditional histology. In this project, the student will apply three-color SRS histology, which can map both cells and extracellular matrix, on breast tissue and utilize the result to achieve high-accuracy malignancy evaluation of the tissue through machine learning.

LABORATORY MENTOR
Hongli Ni

RESEARCH GOALS
– Apply three-color stimulated Raman histology to breast tissue.
– Achieve high-accuracy classification of cancerous and healthy breast tissue with machine learning algorithm.

LEARNING GOALS
– Learn the principle of stimulated Raman scattering (SRS) imaging, operation of SRS imaging system, hyperspectral and multi-color SRS data processing
– Machine learning for SRS-image-based classification.

Learn more about Professor Cheng on his faculty page.