Dr. Xiaohan Xing | Engineering | Young Scientist Award

Dr. Xiaohan Xing, Stanford University, United States.

Professional Profile

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🎓 Early Academic Pursuits

Dr. Xiaohan Xing began his academic journey at Shandong University, where he pursued a B.E. in Biomedical Engineering with outstanding academic performance, ranking 1st among 200+ students. His undergraduate thesis, Bleeding Detection in Wireless Capsule Endoscopy Images, earned him the Outstanding Undergraduate Thesis Award. Following this, he pursued a Ph.D. in Electronic Engineering at The Chinese University of Hong Kong (CUHK) under the supervision of Prof. Max Q.-H. Meng and Prof. Hongsheng Li. His doctoral research focused on efficient deep learning algorithms for disease diagnosis, a field at the intersection of medical imaging, artificial intelligence, and healthcare.

💼 Professional Endeavors

Dr. Xing has held prestigious postdoctoral positions at leading institutions. He is currently a Postdoctoral Fellow at Stanford University’s Department of Radiation Oncology, working under Prof. Lei Xing. Prior to this, he served as a Postdoctoral Fellow at City University of Hong Kong (CityU), collaborating with Prof. Yixuan Yuan. He also gained industry experience as a Research Intern at Tencent AI Healthcare, where he worked under Dr. Jianhua Yao and Dr. Fan Yang to develop AI-driven healthcare solutions.

🔬 Contributions and Research Focus On Biomedical Engineering

Dr. Xing’s research is centered on Biomedical AI, integrating medical imaging, deep learning, omics analysis, and data science to enhance clinical decision-making. His key research contributions include:

• Data-efficient algorithms for medical image analysis 🏥

• Interpretable AI models for omics data analysis 🧬

• Multi-modal learning approaches integrating medical images and genomic data 🖼️➕📊

• Trustworthy AI models for precision medicine applications 🏥

His innovative solutions have tackled challenges such as modality gaps in multi-modal learning, low-field MRI reconstruction, small lesion detection in endoscopy images, and cancer survival prediction. His research outputs have been published in top-tier journals such as Medical Image Analysis (MedIA), IEEE Transactions on Medical Imaging (TMI), and Bioinformatics.

🌍 Impact and Influence

Dr. Xing’s work significantly impacts precision medicine and AI-assisted healthcare. His publications in top-tier journals and conferences, including Medical Image Analysis (MedIA) and MICCAI, showcase his groundbreaking innovations. He has also contributed to patents in AI-driven disease detection.

🏅 Awards and Honors 

Dr. Xing’s excellence in research has been recognized with prestigious awards, including:

• ASTRO Best of Physics 2024 (Ranked 1st among medical imaging papers, top 5 out of 1,650).

• MICCAI Young Scientist Award 2022 (Equivalent to Best Paper Award, only 5 recipients worldwide).

• National Scholarship (China Ministry of Education, Top 1%), the highest honor for students in China.

• Outstanding Undergraduate Thesis Award (SDU, Top 1%).

• First-class and Full Postgraduate Scholarships, recognizing academic excellence.
  These honors highlight his exceptional contributions to medical AI and deep learning, positioning him as a rising leader in the field.

🎒 Teaching and Mentorship

Dr. Xing has actively contributed to academia as a teaching assistant for:

• Digital Circuits and Systems
• Linear Algebra for Engineers
• Digital Image Processing

Additionally, he has mentored Ph.D. and master’s students from CUHK, UCLA, and the University of Chinese Academy of Sciences.

📚 Academic Citations

Dr. Xing has been recognized in leading scientific communities, serving as an Area Chair for MICCAI 2024 and a reviewer for IEEE TMI, IEEE TIP, and MedIA. His papers are highly cited, reflecting his influence in AI-powered medical research.

🚀Future Vision and Impact

Dr. Xing envisions pioneering AI-driven healthcare by developing trustworthy and interpretable AI models. His future work aims to bridge the gap between medical imaging and omics data for next-generation precision medicine. As a rising leader in Biomedical AI, his legacy is set to transform medical diagnostics and patient care.

Publications Top Notes

 

• 🏥 Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction
  📖 IEEE Transactions on Automation Science and Engineering
  👥 Cited by: 108 📆 Year: 2020

• 🔬 DT-MIL: Deformable Transformer for Multi-Instance Learning on Histopathological Images
  📖 Medical Image Computing and Computer Assisted Intervention (MICCAI)
  👥 Cited by: 97 📆 Year: 2021

• 🎥 Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon with Deep-Learning-Based Polyp Recognition
  📖 Proceedings of the IEEE
  👥 Cited by: 90 📆 Year: 2019

• 🩺 Zoom in Lesions for Better Diagnosis: Attention Guided Deformation Network for WCE Image Classification
  📖 IEEE Transactions on Medical Imaging
  👥 Cited by: 48 📆 Year: 2020

• 🏥 Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification
  📖 Medical Image Computing and Computer Assisted Intervention (MICCAI)
  👥 Cited by: 45 📆 Year: 2021

• 🩸 Bleeding Detection in Wireless Capsule Endoscopy Image Video Using Superpixel-Color Histogram and a Subspace KNN Classifier
  📖 IEEE Engineering in Medicine and Biology Society Conference
  👥 Cited by: 44 📆 Year: 2018

• 🧬 Multi-Level Attention Graph Neural Network Based on Co-Expression Gene Modules for Disease Diagnosis and Prognosis
  📖 Bioinformatics
  👥 Cited by: 40 📆 Year: 2022

• 🏥 Multi-Modal Multi-Instance Learning Using Weakly Correlated Histopathological Images and Tabular Clinical Information
  📖 Medical Image Computing and Computer Assisted Intervention (MICCAI)
  👥 Cited by: 33 📆 Year: 2021

• 🧠 Discrepancy and Gradient-Guided Multi-Modal Knowledge Distillation for Pathological Glioma Grading
  📖 Medical Image Computing and Computer-Assisted Intervention (MICCAI)
  👥 Cited by: 28 📆 Year: 2022

• 🩸 A Saliency-Aware Hybrid Dense Network for Bleeding Detection in Wireless Capsule Endoscopy Images
  📖 IEEE International Symposium on Biomedical Imaging (ISBI)
  👥 Cited by: 21 📆 Year: 2019

• 🤖 Medical Federated Learning with Joint Graph Purification for Noisy Label Learning
  📖 Medical Image Analysis
  👥 Cited by: 18 📆 Year: 2023

• 🩸 Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images
  📖 Medical Image Computing and Computer-Assisted Intervention (MICCAI)
  👥 Cited by: 18 📆 Year: 2022

• 🧬 An Interpretable Multi-Level Enhanced Graph Attention Network for Disease Diagnosis with Gene Expression Data
  📖 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
  👥 Cited by: 17 📆 Year: 2021

• 👁 Boundary-Enhanced Semi-Supervised Retinal Layer Segmentation in Optical Coherence Tomography Images Using Fewer Labels
  📖 Computerized Medical Imaging and Graphics
  👥 Cited by: — 📆 Year: 2023