Faculty Mentor
About Xiaqi
I received my Ph.D. in Computer Science from Chongqing University in 2019, focusing on deep learning and medical image processing, particularly gastrointestinal tract endoscopy images. From 2019 to 2021, I was a postdoctoral researcher at Vanderbilt University, where I worked on skin cancer image analysis to support early detection and classification. In May 2024, I completed a Master of Arts in Intercultural Studies at Logos Evangelical Seminary. After my baptism in December 2019, I developed a deep passion for mission and have since served college students, graduate students, and postdoctoral fellows through hospitality and discipleship. I enjoy opening my home, building meaningful relationships, and sharing the gospel of Jesus Christ. My current interests center on the church’s mission in the age of artificial intelligence (AI 2.0). With a background in both technology and cross-cultural ministry, I am passionate about how the church can engage with emerging technologies.
Education
Logos Evangelical Seminary
2024
Master of Arts in Intercultural Studies
Chongqing University
2019
PhD Computer Science
Research Interests
Artificial Intelligence, Church's mission
Publications
Fine-tuning pre-trained convolutional neural networks for gastric precancerous disease classification on magnification narrow-band imaging images
Neurocomputing 392 (2020): 253-267.
Transfer learning with convolutional neural network for early gastric cancer classification on magnifiying narrow-band imaging images
In 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1388-1392. IEEE, 2018.
Hue-texture-embedded region-based model for magnifying endoscopy with narrow-band imaging image segmentation based on visual features
Computer methods and programs in biomedicine 145 (2017): 53-66.
Baseline photos and confident annotation improve automated detection of cutaneous graft-versus-host disease
Clinical hematology international 3, no. 3 (2021): 108-115.
Detecting PHG frames in wireless capsule endoscopy video by integrating rough global dominate-color with fine local texture features
In Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, pp. 480-493. SPIE, 2018.
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