Zihan Deng | Artificial Intelligence | Best Researcher Award

Published on by AI Data Scientist

Dr. Zihan Deng | Artificial Intelligence | Best Researcher Award

Harbin Institute of Technology, China

Zihan Deng is a young and accomplished researcher in the field of imaging technology and computational tomography, with a strong foundation in deep learning and artificial intelligence. With a robust academic background and an array of interdisciplinary experiences, Deng has made significant contributions through high-impact publications, competitive grants, and patents. His expertise lies at the intersection of optical instrumentation and medical image analysis, and he continues to actively engage in scientific exploration with promising results.

Profile

Orcid

Education

Deng completed his undergraduate studies in Computer Science and Technology at Harbin Engineering University (2019–2023), ranking in the top 5% of his class. His academic curriculum included rigorous coursework in mathematics and computer science, scoring consistently above 90 in core subjects. He was subsequently recommended for direct admission into the graduate program at Harbin Institute of Technology, where he is currently pursuing his Master’s degree at the Institute of Ultra-Precision Optical Instrument Engineering under the mentorship of Professor Junning Cui and Academician Jiubin Tan. His research spans CT reconstruction, deep learning-based image enhancement, and X-ray detection technologies.

Experience

Deng has accumulated diverse experience through internships and collaborative projects. He served in leadership roles within student organizations and academic competitions, including receiving awards in national-level modeling and software contests. He undertook summer research at Tsinghua University’s IDG/McGovern Brain Research Institute and was later selected to join Germany’s PTB “Chief Engineer Class” as a visiting scholar. Professionally, he interned with Chengdu Shuzhilian Technology and Guangzhou CVTE, where he contributed to image processing and video enhancement projects. He has also played key roles in multimillion-yuan research collaborations with institutions like CGN Research Institute and GF High-End Semiconductor Imaging Systems.

Research Interest

Deng’s research interests revolve around imaging technology, deep learning, and CT reconstruction methods. He focuses on developing advanced algorithms for sparse-angle computed tomography, artifact reduction, and multi-view image correction using neural networks. His work integrates domain-specific knowledge from instrumentation science with state-of-the-art machine learning frameworks to improve image quality in both medical diagnostics and industrial inspection. He also investigates beam hardening correction and reconstruction under large field-of-view (FOV) conditions, addressing challenges in high-precision imaging systems.

Award

Over the course of his academic journey, Deng has received 11 scholarships and numerous accolades. These include five first-class and two second-class academic scholarships from Harbin Engineering University, the prestigious Xiaomi Scholarship, and the Outstanding Youth League Member Award. His undergraduate thesis on sparse-angle CT reconstruction was selected as an Excellent Graduation Project (top 2%). He has also won national-level awards in competitions such as the Mathematical Modeling Contest and the English Proficiency Championship.

Publication

Deng has authored or co-authored several influential papers in prestigious journals and conferences. His representative publications include:

  1. Deng Z., Wang Z., et al. (2024). “COO-DuDo: Computation Overhead Optimization Methods for Dual-Domain Sparse-View CT Reconstruction”, Expert Systems with Applications (JCR Q1, IF=7.5, in press) – cited in advanced CT algorithm research.

  2. Deng Z., Wang Z., Lin L., Wang S., Cui J. (2024). “Research on the Effectiveness of Multi-View Slice Correction Technology Based on Deep Learning in High-Pitch Spiral Scanning Reconstruction”, Journal of X-Ray Science and Technology (JCR Q2, IF=3.0) – applied in spiral CT systems.

  3. Wang Z.#, Deng Z.#, Liu F., et al. (2023). “OSNet & MNetO for Linear Computed Tomography in Multi-Scenarios”, IEEE Transactions on Instrumentation and Measurement (JCR Q1, IF=5.6) – widely cited in instrumentation imaging.

  4. Deng Z., Deng K., Wang Z., et al.. “Small Class Discussion-Based Teaching in Instrumentation Education”, The International Journal of Education – cited in engineering education reform discussions.

  5. Li Z., Li K., Deng Z., et al. (2024). “Assessment of Sheetlet Thickness in Human Left Ventricular Free Wall Using X-ray Phase-Contrast Microtomography”, Medical Image Analysis (JCR Q1, IF=10.9, accepted) – applied in cardiovascular research.

  6. Deng Z., Wang Z., Lin L., et al. (2025). “Computation Overhead Optimization Dual-Domain Network for Sparse-View CT Reconstruction”, ICASSP 2025 (CCF-B Conference) – in review, expected to support efficient CT image pipelines.

  7. Deng Z., Wang Z., Lin L., Wang S. “Hel-MUNet: Mamba-Unet with Helical Encoding for Clinical High Pitch Helical CT Reconstruction”, MICCAI 2025 (under review) – aligned with cutting-edge clinical imaging methods.

Conclusion

Zihan Deng exemplifies the next generation of research professionals driving innovation in imaging and artificial intelligence. Through a blend of strong theoretical foundation, hands-on project experience, and impactful publications, he has demonstrated exceptional capability in solving complex technical problems. With continued guidance under leading scholars and global exposure, Deng is well-positioned to become a prominent figure in the advancement of smart medical imaging and intelligent instrumentation.

Shoujun Zhou | Artificial Intelligence | Best Scholar Award

Published on by AI Data Scientist

Prof. Shoujun Zhou | Artificial Intelligence | Best Scholar Award

Research Professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Prof. Shoujun Zhou is a distinguished biomedical engineering researcher and a leading figure in the field of medical robotics and image-guided therapy. He currently serves as a specially appointed research professor at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and concurrently holds a professorship at the National Institute for High-Performance Medical Devices. Over his career, Prof. Zhou has led and contributed to numerous national and provincial-level scientific research projects, focusing on developing interventional surgical robotics and advanced medical imaging technologies. His leadership in this interdisciplinary field has positioned him at the forefront of integrating artificial intelligence with minimally invasive therapeutic solutions.

Profile

Orcid

Education

Prof. Zhou’s academic journey began with a Bachelor’s degree in Test and Control from the Air Force Engineering University (1989–1993). He then earned a Master’s degree in Communication and Information Systems from Lanzhou University (1997–2000), further refining his technical expertise. His academic pursuits culminated in a Ph.D. in Biomedical Engineering from Southern Medical University (2001–2004). This multidisciplinary educational background laid a solid foundation for his future contributions in medical imaging, robotics, and computational modeling.

Experience

With over three decades of professional experience, Prof. Zhou has served in multiple prestigious institutions. From 1993 to 2001, he worked as an engineer in the 94921 Military Unit, followed by a postdoctoral tenure at Beijing Institute of Technology. He transitioned to industry in 2007 as an enterprise postdoctoral researcher at Shenzhen Haibo Technology Co., Ltd., and later joined the 458 Hospital of the PLA as a senior engineer. Since 2010, he has been a principal investigator and research professor at SIAT, where he leads a dedicated research team working on the convergence of robotics, imaging, and AI for medical applications.

Research Interest

Prof. Zhou’s research primarily focuses on interventional surgical robots, image-guided therapy, and medical image analysis. He is particularly interested in developing intelligent, minimally invasive systems that combine AI algorithms with real-time imaging for precise diagnostics and interventions. His work includes modeling and segmentation of vascular structures, semi-supervised learning techniques in medical imaging, and the development of surgical robots tailored for procedures such as liver tumor ablation and cardiovascular interventions. He is also actively involved in improving navigation systems that reduce or eliminate radiation exposure in image-guided procedures.

Award

Prof. Zhou’s contributions have been widely recognized both nationally and internationally. He was honored with the “Best Researcher Award” at the Global Awards on Artificial Intelligence and Robotics in 2022, organized by ScienceFather. He also received a Silver Medal in the Global Medical Robot Innovation Design Competition in 2019 for his work on a vascular interventional robotic system. His earlier work earned the Second Prize of Guangdong Provincial Science and Technology Progress Award in 2009 and contributed to a project that received a First-Class Prize in Science and Technology Progress from the Ministry of Education in 2006. These accolades reflect his sustained excellence and impact in the field of medical technology.

Publication

Prof. Zhou has authored over 100 scientific papers, including several published in top-tier journals. Selected key publications include:

  1. Zhang Z. et al. (2024). “Verdiff-Net: A Conditional Diffusion Framework for Spinal Medical Image Segmentation,” Bioengineering, 11(10):1031 – cited in spinal image AI segmentation studies.

  2. Zhang X. et al. (2024). “Automatic Segmentation of Pericardial Adipose Tissue from Cardiac MR Images,” Medical Physics, DOI:10.1002/mp.17558 – referenced for semi-supervised MR image segmentation.

  3. Tian H. et al. (2024). “EchoSegDiff: a diffusion-based model for left ventricular segmentation,” Medical & Biological Engineering & Computing, DOI:10.1007/s11517-024-03255-0 – cited in cardiac echocardiography image modeling.

  4. Li J. et al. (2024). “DiffCAS: Diffusion based Multi-attention Network for 3D Coronary Artery Segmentation,” Signal, Image and Video Processing, DOI:10.1007/s11760-024-03409-5 – relevant in coronary CT imaging analysis.

  5. Wang K.N. et al. (2024). “SBCNet: Scale and Boundary Context Attention for Liver Tumor Segmentation,” IEEE Journal of Biomedical and Health Informatics, 28(5):2854-2865 – cited in liver tumor segmentation research.

  6. Xiang S. et al. (2024). “Automatic Delineation of the 3D Left Atrium from LGE-MRI,” IEEE Journal of Biomedical and Health Informatics, DOI:10.1109/JBHI.2024.3373127 – frequently cited in atrial structural analysis.

  7. Miao J. et al. (2024). “SC-SSL: Self-correcting Collaborative and Contrastive Co-training,” IEEE Transactions on Medical Imaging, 43(4):1347-1364 – referenced in semi-supervised medical image learning.

Conclusion

Prof. Zhou’s work exemplifies the synergy between engineering and medical science, enabling significant advances in minimally invasive diagnosis and treatment. Through his persistent innovation in surgical robotics and medical image computing, he has made a profound impact on the evolution of intelligent healthcare technologies. His dedication to mentoring young researchers and contributing to national and provincial projects reflects a commitment not only to scientific discovery but also to the translation of research into clinical and industrial applications. With a career marked by excellence in research, education, and innovation, Prof. Zhou continues to be a pivotal figure shaping the future of intelligent medicine.

Fatih Kalemkuş | Artificial Intelligence | Best Researcher Award

Published on by AI Data Scientist

Assist. Prof. Dr. Fatih Kalemkuş | Artificial Intelligence | Best Researcher Award

Assistant Professor at Kafkas University, Turkey

Dr. Fatih Kalemkuş is an Assistant Professor at Kafkas University, where he specializes in Electronic Commerce and Technology Management. With a rich academic and professional background, Dr. Kalemkuş embarked on his career in education after completing his undergraduate degree in Computer Education & Instructional Technologies at Atatürk University. He has taught various subjects related to information technology, first as an Informatics Technologies Teacher at the Turkish Ministry of National Education and later as a lecturer at Kafkas University’s Distance Education Application and Research Center. His journey culminated in earning a doctoral degree from Fırat University in Computer Education & Instructional Technologies, where he was honored with the “Most Successful Doctoral Thesis” award in 2024.

Profile

Orcid

Education

Dr. Kalemkuş’s educational journey began at Erzincan Fatih Industrial Vocational High School, where he pursued studies in the Computer Department. He continued to develop his academic career by earning his bachelor’s degree in 2006 from Atatürk University in the field of Computer Education & Instructional Technologies. He then completed a Master’s degree in Internet and Informatics Technologies Management from Afyon Kocatepe University between 2014 and 2016. His dedication to advancing his knowledge in the field led him to pursue a Ph.D. at Fırat University, graduating in 2023 with a focus on Computer Education & Instructional Technologies. His research has been instrumental in advancing educational practices in the digital age, with a specific focus on artificial intelligence and emerging technologies.

Experience

Dr. Kalemkuş has had diverse professional experiences. From 2007 to 2021, he served as an Informatics Technologies Teacher under the Turkish Ministry of National Education, shaping the next generation’s skills in information technology. In 2021, he joined Kafkas University as a lecturer at the Distance Education Application and Research Center, where he taught courses related to digital learning tools. His commitment to academic excellence and innovation in education led to his promotion to Assistant Professor in 2024 at Kafkas University’s Electronic Commerce and Technology Management Department, where he continues to make impactful contributions to research and education.

Research Interests

Dr. Kalemkuş’s research focuses on key areas of educational technology and digital transformation. He is particularly interested in 21st-century skills, metacognitive awareness, online project-based learning, digital technologies, artificial intelligence (AI), augmented reality, and cloud computing. He also explores the intersection of education and emerging technologies, such as natural language processing (NLP) and the integration of AI in educational contexts. His work aims to improve learning outcomes and foster innovation in teaching methodologies. His ongoing research projects delve into the development of AI-driven educational materials and interactive learning environments that enhance students’ academic engagement.

Awards

Dr. Kalemkuş has received recognition for his outstanding academic contributions. In 2024, he was honored with the prestigious “Most Successful Doctoral Thesis” award from Fırat University for his exceptional research and academic achievements. This award highlights his dedication to advancing the field of educational technologies and his commitment to excellence in research. His work, particularly on the use of AI in education, has positioned him as a leading researcher in his field.

Publications

Dr. Kalemkuş has authored several influential publications in well-regarded journals and books. His research has been featured in leading SSCI and ESCI journals, including the European Journal of Education, Interactive Learning Environments, Science & Education, and Journal of Research in Special Educational Needs. His recent publications include:

Kalemkuş, F., & Kalemkuş, J. (2025). “Primary School Students’ Perceptions of Artificial Intelligence: Metaphor and Drawing Analysis”, European Journal of Education, 60(1), 1-23.

Kalemkuş, F., & Bulut-Özek, M. (2024). “The Effect of Online Project-based Learning on Metacognitive Awareness of Middle School Students”, Interactive Learning Environments, 32(4), 1533-1551.

Kalemkuş, F., & Kalemkuş, J. (2024). “The Effect of Designing Scientific Experiments with Visual Programming on Learning Outcomes”, Science & Education, 1-23.

Kalemkuş, F., & Bulut-Özek, M. (2023). “Effect of the Use of Augmented Reality Applications on Academic Achievement in Science Education: Meta Analysis”, Interactive Learning Environments, 31(9), 6017-6034.

Kalemkuş, F. (2024). “Trends in Instructional Technologies Used in Education for People with Special Needs Due to Intellectual Disabilities and Autism”, Journal of Research in Special Educational Needs, 1-25.

Kalemkuş, F., & Çelik, L. (2023). “Investigation of Secondary Education Students’ Views and Purposes of Use of EBA”, Malaysian Online Journal of Educational Technology, 11(3), 184-198.

Kalemkuş, F., & Bulut-Özek, M. (2021). “Research Trends in 21st Century Skills: 2000-2020”, MANAS Sosyal Araştırmalar Dergisi, 10(2), 878-900.

Conclusion

Dr. Fatih Kalemkuş’s career has been marked by a profound commitment to advancing educational technology and promoting the use of emerging technologies in learning environments. With numerous publications in prestigious journals and books, he has made a significant impact on the fields of AI, digital learning, and 21st-century skills development. His work continues to shape the educational landscape, particularly in the integration of innovative digital tools to enhance teaching and learning outcomes. Dr. Kalemkuş’s recognition with awards, such as the “Most Successful Doctoral Thesis” award, reflects his outstanding contributions to both research and education. His interdisciplinary approach ensures that his work will remain at the forefront of educational innovations for years to come.

Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Published on by AI Data Scientist

Dr. Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Researcher | Central People’s Hospital of Zhanjiang | China

Dr. Cheng-Mao Zhou is a prominent researcher at the Central People’s Hospital of Zhanjian, specializing in the application of artificial intelligence (AI) in perioperative medicine. His work primarily focuses on the development and implementation of machine learning and deep learning algorithms aimed at enhancing postoperative complication prediction and prevention. Dr. Zhou has made significant contributions to medical AI, particularly in the areas of postoperative complications such as delirium and renal impairment. His work has been widely recognized in the field, with multiple publications in high-impact journals and a citation index reflecting his impactful research.

Profile

Scopus

Education

Dr. Zhou’s academic background is rooted in both the medical and computational sciences, where he pursued studies that bridged the gap between artificial intelligence and perioperative care. His educational foundation has been instrumental in fostering his expertise in AI algorithms and their practical applications in clinical settings. Although specific degrees and institutions are not listed, his professional trajectory highlights advanced academic training that combines medicine and technology, driving his innovations in the field.

Experience

Dr. Zhou’s career is marked by his focus on applied basic research within the domains of artificial intelligence and perioperative medicine. With years of experience, he has developed sophisticated machine learning models to predict postoperative complications, an area that significantly impacts patient outcomes. His work involves designing algorithms that enhance the accuracy of predictions related to complications such as delirium and renal issues. Dr. Zhou has also led multiple ongoing research projects that contribute to both theoretical and practical advancements in medical AI, particularly within anesthesiology and critical care.

Research Interests

Dr. Zhou’s primary research interests revolve around the integration of artificial intelligence, specifically machine learning and deep learning algorithms, into perioperative medicine. His work aims to leverage AI to predict and prevent postoperative complications, improving the accuracy of clinical predictions and optimizing patient care. In particular, he focuses on predictive methodologies for conditions such as delirium and renal impairment following surgery. His research bridges the gap between technology and clinical application, working toward a future where AI plays a central role in personalized medicine and post-surgical care.

Awards

Dr. Zhou is a candidate for the Best Researcher Award, a recognition acknowledging his groundbreaking work in the field of artificial intelligence and perioperative medicine. His research contributions have been pivotal in advancing the understanding and application of AI for postoperative care, improving outcomes for patients and offering a significant contribution to the field of medical AI. Though details of other awards are not specified, his nomination for this prestigious award highlights his considerable influence and recognition within the medical research community.

Publications

Dr. Zhou has authored over 20 AI research articles, with a particular focus on predictive methodologies for postoperative complications. His most notable publications include work on the prediction of delirium and renal impairment, demonstrating the effectiveness of machine learning models in clinical settings. Below is a selection of his key publications:

“A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm”

    • Authors: Zhou, C.-M., Xue, Q., Li, H., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 0

“Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery”

    • Authors: Zhou, C.-M., Li, H., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 3

“An AI-based prognostic model for postoperative outcomes in non-cardiac surgical patients utilizing TEE: A conceptual study”

    • Authors: Zhu, Y., Liang, R., Zhou, C.-M.
    • Year: 2024
    • Citations: 0

“Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 6

“Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology”

    • Authors: Zhou, C.-M., Wang, Y., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 10

“A long duration of intraoperative hypotension is associated with postoperative delirium occurrence following thoracic and orthopedic surgery in elderly”

    • Authors: Duan, W., Zhou, C.-M., Yang, J.-J., Ma, D.-Q., Yang, J.-J.
    • Year: 2023
    • Citations: 19

“Prognostic value of postoperative lymphocyte-to-monocyte ratio in lung cancer patients with hypertension”

    • Authors: Yuan, M., Wang, P., Meng, R., Zhou, C., Liu, G.
    • Year: 2023
    • Citations: 0

“Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Zhu, Y.
    • Year: 2023
    • Citations: 5

“Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I–II: a retrospective cohort study”

    • Authors: Li, Z., Liang, L., Duan, W., Zhou, C., Yang, J.-J.
    • Year: 2022
    • Citations: 2

“Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2022
    • Citations: 16

Conclusion:
Dr. Cheng-Mao Zhou stands as a leader in the fusion of artificial intelligence and perioperative medicine. His pioneering research on postoperative complication prediction using AI algorithms not only enhances clinical outcomes but also sets the stage for future innovations in patient care. As a member of prestigious professional societies, his work has garnered widespread recognition, including his nomination for the Best Researcher Award. Dr. Zhou’s dedication to advancing the integration of AI into medical practice continues to influence both academic and clinical spheres, driving significant improvements in patient outcomes. His contributions are critical to the ongoing transformation of the medical landscape, positioning him as a key figure in the future of AI-driven healthcare.

Guangbo Yu | Artificial Intelligence | Best Researcher Award

Published on by AI Data Scientist

Mr. Guangbo Yu | Artificial Intelligence | Best Researcher Award

Mr .Guangbo  Yu, PhD Student, University of California, United States.

Mr. Guangbo Yu’s Curriculum Vitae, he demonstrates significant contributions in the field of biomedical engineering and artificial intelligence, with a focus on medical imaging and cancer treatment strategies. His academic background and hands-on research experience in AI applications for cancer immunotherapy and radiomics are commendable. Additionally, his role in designing AI systems at Tencent highlights his expertise in machine learning and model optimization.

Profile

google scholar

🎓 Education:

PhD in Biomedical Engineering (Expected 2027)

University of California, Irvine

Specialization: Radiological Science

Advisor: Prof. Zhuoli Zhang

Master’s in Computer Science

University of Southern California (2015–2017)

Bachelor’s in Software Engineering

University of Electronic Science and Technology of China (2011–2015)

🔬 Research Experience:

Graduate Assistant Researcher at UC Irvine (2022–Present)

Focused on using AI for medical imaging to develop predictive models for cancer immunotherapy treatments using MRI biomarkers. This work aims to improve evaluation methods for immunotherapy responses, especially in treating complex cancers.

💼 Professional Experience:

AI Engineer at Tencent QTrade (2020–2022)

Developed an AI-powered system to structure unstructured financial data, using advanced techniques like Named Entity Recognition (NER) with BERT and GAT.

Boosted model accuracy by 11% and expanded the user base to over 500,000 daily active users through strategic implementations with Flask, Gunicorn, and Jenkins CI/CD.

🔍 Research Interests:

Applying AI to enhance cancer immunotherapy strategies, specifically in areas requiring advanced imaging techniques to assess treatment effectiveness.

Citations:

Citations: 12 (all since 2019)

h-index: 2 (a minimum of two papers with at least two citations each)

i10-index: 0 (no papers with 10 or more citations)

📖 Publications and Presentations:

Qtrade AI at SemEval-2022 Task 11: A Unified Framework for Multilingual NER Task

W. Gan, Y. Lin, G. Yu, G. Chen, & Q. Ye. (2022). Association for Computational Linguistics.

Sorafenib Plus Memory-Like Natural Killer Cell Combination Therapy in Hepatocellular Carcinoma

A. Eresen, Y. Pang, Z. Zhang, Q. Hou, Z. Chen, G. Yu, Y. Wang, V. Yaghmai, … (2024). American Journal of Cancer Research, 14(1), 344.*

Dendritic Cell Vaccination Combined with Irreversible Electroporation for Treating Pancreatic Cancer—A Narrative Review

Z. Zhang, G. Yu, A. Eresen, Z. Chen, Z. Yu, V. Yaghmai, Z. Zhang. (2024). Annals of Translational Medicine.

MRI Radiomics to Monitor Therapeutic Outcome of Sorafenib Plus IHA Transcatheter NK Cell Combination Therapy in Hepatocellular Carcinoma

G. Yu, Z. Zhang, A. Eresen, Q. Hou, E. E. Garcia, Z. Yu, N. Abi-Jaoudeh, … (2024). Journal of Translational Medicine, 22(1), 76.*

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer

G. Yu, Z. Zhang, A. Eresen, Q. Hou, F. Amirrad, S. Webster, S. Nauli, … (2024). International Journal of Molecular Sciences, 25(22), 12038.*

Sorafenib Plus Memory-Like Natural Killer Cell Immunochemotherapy Boosts Treatment Response in Liver Cancer

A. Eresen, Z. Zhang, G. Yu, Q. Hou, Z. Chen, Z. Yu, V. Yaghmai, Z. Zhang. (2024). BMC Cancer, 24(1), 1215.*

Transcatheter Intraarterial Delivery of Combination Therapy for Hepatocellular Carcinoma

Z. Zhang, A. Eresen, G. Yu, K. Liu, Q. Hou, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S199.*

Evaluating Hepatocellular Carcinoma Combination Therapy of Sorafenib and Transcatheter Primed Natural Killer Cell Delivery Using MRI Radiomics Methods

G. Yu, A. Eresen, Z. Zhang, K. Liu, Q. Hou, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S143–S144.*

Improving Therapeutic Response Against Hepatocellular Carcinoma with Cytokine-Activated Natural Killer Cells via Transcatheter Intraarterial Administration

A. Eresen, Z. Zhang, G. Yu, Q. Hou, N. Abi-Jaoudeh, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S152.*

Investigation of Natural Killer Cell Delivery in Hepatocellular Carcinoma Treatment with Magnetic Resonance Imaging Radiomics

K. Liu, G. Yu, Z. Zhang, Q. Hou, V. Yaghmai, A. Eresen. (2024). Journal of Vascular and Interventional Radiology, 35(3), S92.*

MRI Monitoring of Combined Therapy with Transcatheter Arterial Delivery of NK Cells and Systemic Administration of Sorafenib for the Treatment of HCC

Z. Zhang, G. Yu, A. Eresen, Q. Hou, V. Yaghmai, Z. Zhang. (2024). American Journal of Cancer Research, 14(5), 2216.*