Y.S. Lisa Cheng

ORCID: 0009-0005-8925-2443
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About
Contact & Profiles
Research Areas
  • Oral Health Pathology and Treatment
  • Photodynamic Therapy Research Studies
  • Salivary Gland Tumors Diagnosis and Treatment
  • Optical Imaging and Spectroscopy Techniques
  • Fluorine in Organic Chemistry
  • Head and Neck Cancer Studies
  • Network Security and Intrusion Detection
  • Laser Applications in Dentistry and Medicine
  • Security and Verification in Computing
  • Nanoplatforms for cancer theranostics
  • Advanced Malware Detection Techniques

National Taiwan University of Science and Technology
2023

Texas A&M University
2016-2022

Baylor University
2016

Endogenous FLIM images are being acquired in vivo from patients undergoing tissue biopsy of oral lesions. Preliminary results 20 strongly suggest the potential endogenous for detecting pre-cancer/cancer benign conditions.

10.1364/cancer.2016.cth4a.3 article EN Biomedical optics 2016-01-01

With the growth of Internet Things devices, number and complexity these devices are increasing rapidly. Nevertheless, many IoT products developed without sufficient consideration for security, leaving them vulnerable to exploitation by malware. To proactively address vulnerabilities before they discovered malicious attackers, information security researchers use both static dynamic analysis techniques identify propose firmware updates.

10.1145/3605758.3623493 article EN 2023-11-23

Cancer development in oral epithelial tissue induces subtle changes autofluorescence that are associated with increased metabolic activity malignant cells. These biomarkers of cancer progression include a decrease the optical "redox ratio", defined as intensity NADH divided by FAD, and specific fluorescence lifetime both FAD. We therefore hypothesized more dysplasia can accurately be quantified endogenous imaging (FLIM). In this work, FLIM images benign, dysplastic early stage cancerous...

10.1117/12.2293794 article EN 2018-03-14

Multispectral autofluorescence endoscopy is a non-invasive optical imaging modality that can provide contrast between malignant and benign oral tissue. We hypothesized discrimination of cancerous precancerous from lesions be achieved through machine-learning (ML) models developed with multispectral intensity features. In vivo endoscopic images benign, precancerous, were acquired 67 patients used to optimize ML for cancerous/precancerous lesions. This study demonstrates the potentials...

10.1117/12.2608843 article EN 2022-03-04
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