Qi Lai | Medical Image Analysis | Women Researcher Award

Dr. Qi Lai | Medical Image Analysis | Women Researcher Award

Assistant Professor | Shenzhen Institutes of Advanced Technology | China

Dr. Qi Lai is a researcher at the Shenzhen Institutes of Advanced Technology, China, specializing in weakly supervised learning, medical image analysis, and multi-instance learning. He has authored 11 peer-reviewed publications, receiving over 52 citations with an h-index of 5. His work spans deep learning for pathology, object detection, semantic segmentation, and medical image restoration, often in collaboration with international teams across leading institutions. Notable contributions include interactive and hybrid MIL frameworks and contrastive learning methods that enhance diagnostic precision. His research advances reliable AI-driven clinical decision support, contributing to improved healthcare technologies and societal well-being.

 

Citation Metrics (Scopus)

200

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50

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Citations
52

Documents
11

h-index
5

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications


Fast broad multiview multi-instance multilabel learning (FBM3L) with viewwise intercorrelation.

– IEEE Transactions on Neural Networks and Learning Systems (2024). Cited By: 5 

Interactive multiple instance learning network for whole slide image analysis.

– Expert Systems with Applications. (2026). Cited By:  1

Joint discriminative latent subspace learning for image classification

– IEEE Transactions on Circuits and Systems for Video Technology. (2022). Cited By:: 15

Shihao Wang | Semantic Segmentation | Best Researcher Award

Mr. Shihao Wang | Semantic Segmentation | Best Researcher Award

Xinjiang University, China

Author Profiles

Scopus

🎓 Education Background

Mr. Shihao Wang completed his undergraduate studies at the North China Institute of Science and Technology from September 2016 to June 2020, earning a Bachelor of Science in Information Management & Information Systems. He then pursued graduate studies at Xinjiang University, where he obtained his Master’s degree in Computer Technology from September 2021 to May 2024. His academic path reflects a solid foundation in both information systems and advanced computing technologies.

đź§  Professional Skills

Mr. Wang is highly proficient in Python and PyTorch, with extensive experience in data processing, model training, and optimization. He is familiar with CUDA for parallel computing, enabling efficient use of GPU resources in deep learning tasks. His technical toolkit includes Linux command-line operations and shell scripting, which he leverages for server management and deployment processes. He has hands-on experience with model deployment, including the implementation of large models such as Deepseek with WebUI, and is well-versed in model compression techniques like distillation and quantization to optimize resource usage without sacrificing accuracy. In addition to technical skills, Mr. Wang has contributed to the preparation of bidding documents and maintains a keen awareness of emerging technologies in the field of computing power and infrastructure.

đź’Ľ Work Experience

Since July 2024, Mr. Wang has been working at the Cloud Network Operation Center of China Telecom, Urumqi Branch. His responsibilities include the operation and maintenance of cloud platforms and IDC data centers. He oversees equipment migrations, system upgrades, and optimization tasks to ensure the stability and efficiency of infrastructure operations. He has also been involved in computing power and chip technology planning, contributing to the development of resource allocation strategies and system architecture design. Mr. Wang plays a pivotal role in project delivery and technical support, providing key input in projects like the Xinjiang Intelligent Computing Center and the Yan’an Road Data Center, where he has helped with technical proposals, equipment quotations, and bidding processes.

Among his notable projects is the development of a Real 3D Mixed Reality Scene Construction System using NeRF (Neural Radiance Fields) and 3D Gaussian Splatting. This initiative, under an edge-cloud collaborative architecture, combines high-performance terminal data acquisition with cloud-based model optimization for real-time rendering. Mr. Wang also contributed to the cloud service upgrade for the Xinjiang Party School and Xinjiang Science and Technology Press, executing system updates and centralized management of terminal devices. Additionally, he was a part of the Xinjiang Integrated Government Service Platform reconstruction, working in collaboration with the Autonomous Region’s Digital Development Bureau to optimize user portals and service modules.

đź§Ş Project Experience

In 2018–2019, during his undergraduate studies, Mr. Wang participated in the development of the “Baiyinhuo Emergency Management System” under the guidance of his academic mentors and a doctoral student from China University of Mining and Technology. This project laid the groundwork for his interest in complex system design. As part of his master’s program, he was involved in a National Key R&D sub-project, focusing on edge-cloud collaboration for mixed reality applications, which incorporated advanced AI technologies such as NeRF. He also filed for a software copyright for his work on the “Scene Segmentation System Based on Transformer”. In parallel, Mr. Wang served as a member of the CCF Xinjiang University Chapter, where he supported academic conference organization and contributed to various professional activities coordinated by his supervisors.

🏆 Honors & Certifications

Mr. Wang has been recognized for his academic excellence and leadership. In 2018, he was awarded a Third-Class Scholarship for his performance. That same year, he was elected Deputy Minister of the Student Union and honored as an Outstanding Cadre. Upon completion of his undergraduate studies in 2020, he was named an Excellent Presenter during his thesis defense. In 2023, he successfully passed the CET-6 (College English Test Band 6), demonstrating his proficiency in academic English.

Notable Publications📝


đź“„ HyperSegmenter: Reappraising the potential of large kernel CNN architecture in efficient semantic segmentation

Authors: Shihao Wang, Zhengxing Huang, Xirali Ablat, Alimjan Aysa, Kurban Ubul

Journal: Expert Systems with Applications

Year: 2025