Giuseppe Placidi | Medical Imaging | Best Research Article Award

Assoc. Prof. Dr. Giuseppe Placidi | Medical Imaging | Best Research Article Award

Professor | University of L’Aquila | Italy

Assoc. Prof. Dr. Giuseppe Placidi is an accomplished researcher whose work spans artificial intelligence, biomedical engineering, and human-computer interaction, with a strong emphasis on translational applications. His research demonstrates a rare combination of methodological innovation and practical impact, exemplified by his development of a lightweight convolutional neural network (CNN) for detecting COVID-19 from chest CT scans, which offers rapid and accurate diagnostic capabilities in clinical settings. In addition, he has contributed significantly to emotion recognition in human-robot interaction, advancing understanding of how AI systems can interpret and respond to human affective states. His work on EEG-based brain-computer interfaces driven by self-induced emotions highlights his expertise in integrating neurophysiological data with real-time computational algorithms, paving the way for more responsive and adaptive BCI systems. Beyond AI and neuroengineering, he has investigated neurocognitive function using semi-immersive virtual reality tasks combined with functional near-infrared spectroscopy, revealing insights into prefrontal cortex activation during complex motor tasks. Furthermore, his clinical research on gender differences in osteoporosis contributes to the understanding of disease mechanisms and patient-specific healthcare strategies. Published in high-impact journals such as Pattern Recognition Letters, Frontiers in Robotics and AI, and Computer Methods and Programs in Biomedicine, Dr. Placidi’s work is widely cited and recognized for its scientific rigor, interdisciplinary breadth, and societal relevance. His research consistently bridges cutting-edge computational methods with real-world applications, making him an exemplary candidate for recognition in research excellence.

Profile: Scopus | ORCID | Google Scholar

Featured Publications

Polsinelli, M., Cinque, L., & Placidi, G. (2020). A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recognition Letters, 140, 95–100.

Spezialetti, M., Placidi, G., & Rossi, S. (2020). Emotion recognition for human-robot interaction: Recent advances and future perspectives. Frontiers in Robotics and AI, 7, 532279.

Iacoviello, D., Petracca, A., Spezialetti, M., & Placidi, G. (2015). A real-time classification algorithm for EEG-based BCI driven by self-induced emotions. Computer Methods and Programs in Biomedicine, 122(3), 293–303.

Moro, S. B., Bisconti, S., Muthalib, M., Spezialetti, M., Cutini, S., Ferrari, M., … Placidi, G. (2014). A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: A functional near-infrared spectroscopy study. NeuroImage, 85, 451–460.

De Martinis, M., Sirufo, M. M., Polsinelli, M., Placidi, G., Di Silvestre, D., & Ginaldi, L. (2020). Gender differences in osteoporosis: A single-center observational study. The World Journal of Men’s Health, 39(4), 750.

Mr. Yunxiang Li | Radiomics Award | Best Researcher Award

Mr. Yunxiang Li | Radiomics Award | Best Researcher Award

Mr. Yunxiang Li, UT Southwestern Medical Center, United States

Yunxiang Li is a PhD student of Medical Physics at the University of Texas Southwestern Medical Center, with a Bachelor’s degree in Computer Science and Technology from Hangzhou Dianzi University. During his tenure at Hangzhou Dianzi University, he collaborated with leading medical institutions, contributing to automatic diagnosis research in root canal therapy. His subsequent work at the IDEA Lab, University of North Carolina at Chapel Hill, focused on infant brain segmentation, resulting in a significant publication in MICCAI. Currently, at the MAIA Lab, UT Southwestern Medical Center, he leads various projects in medical image analysis, including pioneering research on diffusion models and multimodal segmentation techniques. Yunxiang has demonstrated exceptional productivity with numerous first-author papers published and under review in top-tier journals and conferences. His contributions to the field have been widely recognized, evident in his impactful projects such as Chatdoctor and LViT.

Professional Profile:

Scopus

Google Scholar

🎓 Education:

Yunxiang Li is currently pursuing his Ph.D. in Medical Physics at the University of Texas Southwestern Medical Center in Dallas, USA, a program he began in 2022. Prior to this, he completed his Bachelor’s degree in Computer Science and Technology at Hangzhou Dianzi University in Hangzhou, China, from 2018 to 2022. In 2019, Yunxiang expanded his academic horizons as a Visiting Student at The University of Adelaide in Adelaide, Australia. With a diverse educational background spanning computer science and medical physics, Yunxiang is well-equipped to undertake innovative research at the intersection of technology and healthcare.

🔬 Research Interests:

Yunxiang Li’s research interests revolve around the field of Medical Image Analysis, where he delves into various aspects such as Classification, Segmentation, Transformer in vision, Diffusion Model, and LLM. With a keen focus on advancing healthcare technology, Yunxiang’s work aims to enhance the accuracy and efficiency of medical image interpretation and processing. His expertise spans from developing classification algorithms to intricate segmentation techniques, as well as exploring innovative models like the Transformer in vision and the Diffusion Model. Through his research endeavors, Yunxiang seeks to contribute to the improvement of medical diagnostics and treatment planning, ultimately benefitting patients and healthcare professionals

💼 Experience:

Yunxiang Li’s professional journey encompasses diverse research experiences across prestigious institutions. He began his career at the Microelectronics CAD Center of Hangzhou Dianzi University, where he contributed to projects focused on automatic diagnosis of root canal therapy in collaboration with the National Clinical Research Center for Oral Diseases. Following this, Yunxiang joined the IDEA Lab at the University of North Carolina at Chapel Hill, where he worked on infant brain segmentation projects. Currently, he is engaged in groundbreaking research at the MAIA Lab of UT Southwestern Medical Center, focusing on diffusion models and LLM for medical image analysis. With each opportunity, Yunxiang has demonstrated his commitment to advancing knowledge and technology in the field of medical physics.

Publication Top Notes:

  1. Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge
    • Authors: Y Li, Z Li, K Zhang, R Dan, S Jiang, Y Zhang
    • Year: 2023
    • Journal: Cureus
    • Cited By: 204
  2. Fives: A fundus image dataset for Artificial Intelligence based vessel segmentation
    • Authors: K Jin, X Huang, J Zhou, Y Li, Y Yan, Y Sun, Q Zhang, Y Wang, J Ye
    • Year: 2022
    • Journal: Scientific Data
    • Cited By: 63
  3. A Cascade‐SEME network for COVID‐19 detection in chest x‐ray images
    • Authors: D Lv, Y Wang, S Wang, Q Zhang, W Qi, Y Li, L Sun
    • Year: 2021
    • Journal: Medical Physics
    • Cited By: 49
  4. Lvit: Language meets vision transformer in medical image segmentation
    • Authors: Z Li, Y Li, Q Li, P Wang, D Guo, L Lu, D Jin, Y Zhang, Q Hong
    • Year: 2023
    • Journal: IEEE Transactions on Medical Imaging
    • Cited By: 44
  5. GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
    • Authors: Y Li, S Wang, J Wang, G Zeng, W Liu, Q Zhang, Q Jin, Y Wang
    • Year: 2021
    • Conference: MICCAI2021, Machine Learning in Medical Imaging
    • Cited By: 41