Dr. Parisa Kaviani – Radiology – Women Researcher Award

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Dr. Parisa Kaviani - Radiology - Women Researcher Award

Massachusetts General Hospital - United States

AUTHOR PROFILE 

SCOPUS 

ORCID 

GOOGLE SCHOLAR 

🎓 EARLY ACADEMIC PURSUITS

Parisa Kaviani’s journey into the medical field began with her exceptional academic performance at the National Organization for Development of the Exceptionally Talented (NODET) in Neyshabour, Iran, where she completed her high school education from 2002 to 2006. This strong foundation in a highly competitive environment set the stage for her further academic success. Following her passion for medicine, she pursued her Doctor of Medicine (MD) degree at Arak University of Medical Sciences, Iran, from January 2007 to September 2014. Her dedication and academic excellence were evident throughout her medical education, culminating in her graduation with commendable results.

📚 PROFESSIONAL ENDEAVORS

After earning her MD, Parisa Kaviani embarked on a career in clinical practice. She served as a general physician in various emergency departments across Iran from 2014 to 2019, including Fadisheh and Barzanon Health and Treatment Center in Neyshabour, Salmaniyeh Health and Treatment Center in Jiroft, Soltan Abad Hospital, and Davarzan Hospital in Sabzevar. Her roles involved diagnosing and treating patients, managing emergency cases, and providing critical care.

🧠️ CONTRIBUTIONS AND RESEARCH FOCUS

Parisa Kaviani's research career took a significant leap in January 2020 when she joined the Department of Radiology at the University of Southern California Keck School of Medicine as a postdoctoral research fellow. Here, she focused on cancer screening projects aimed at early detection of lung cancer. Continuing her research trajectory, she became a postdoctoral research Radiology fellow at the Department of Radiology, Massachusetts General Hospital, in February 2021. Her research interests pivoted towards the integration of Artificial Intelligence (AI) in radiology, with a particular focus on radiomics and improving radiology quality through deep learning algorithms. Her work in this area contributes to the advancement of AI applications in medical imaging and diagnostics.

🏆 ACCOLADES AND RECOGNITION

Parisa Kaviani’s dedication to the medical field has been acknowledged through various platforms. While specific awards and recognitions are not listed, her progression to prestigious Radiology institutions like Massachusetts General Hospital and Harvard Medical School underscores her expertise and the respect she commands in her field.

🔄 IMPACT AND INFLUENCE

Dr. Kaviani’s work, particularly in AI-driven radiology, has the potential to significantly impact the medical community. By enhancing the accuracy and efficiency of diagnostic imaging, her Radiology contributions aid in early disease detection and improved patient outcomes. Her involvement in high-impact research projects at leading institutions places her at the forefront of medical innovation.

🔮  LEGACY AND FUTURE CONTRIBUTIONS

Parisa Kaviani’s ongoing research and clinical practice continue to pave the way for future advancements in radiology and medical AI. Her commitment to leveraging technology to improve healthcare services ensures that her legacy will be one of innovation and enhanced medical care. As she progresses in her career, her influence is expected to grow, inspiring new methodologies and practices within the medical community.

NOTABLE PUBLICATIONS 

  • Title: Anatomically-Grounded Fact Checking of Automated Chest X-ray Reports
    Authors: Mahmood, R.; Wong, K.C.L.; Reyes, D.M.; D’Souza, N.; Shi, L.; Wu, J.; Kaviani, P.; Kalra, M.; Wang, G.; Yan, P.
    Journal: arXiv
  • Title: Artificial Intelligence Diagnostic Accuracy in Fracture Detection from Plain Radiographs and Comparing it with Clinicians: A Systematic Review and Meta-Analysis
    Authors: Nowroozi, A.; Salehi, M.A.; Shobeiri, P.; Agahi, S.; Momtazmanesh, S.; Kaviani, P.; Kalra, M.K.
    Journal: Clinical Radiology
  • Title: Artificial Intelligence-Generated Smart Impression from 9.8-Million Radiology Reports as Training Datasets from Multiple Sites and Imaging Modalities
    Authors: Kaviani, P.; Kalra, M.K.; Digumarthy, S.R.; Rodriguez, K.; Agarwal, S.; Brooks, R.; En, S.; Alkasab, T.; Bizzo, B.C.; Dreyer, K.J.
    Journal: medRxiv
  • Title: Development and Validation of a Dynamic-Template-Constrained Large Language Model for Generating Fully-Structured Radiology Reports
    Authors: Niu, C.; Kaviani, P.; Lyu, Q.; Kalra, M.K.; Whitlow, C.T.; Wang, G.
    Journal: arXiv
  • Title: Evaluating Automated Radiology Report Quality Through Fine-Grained Phrasal Grounding of Clinical Findings
    Authors: Mahmood, R.; Yan, P.; Reyes, D.M.; Wang, G.; Kalra, M.K.; Kaviani, P.; Wu, J.T.; Syeda-Mahmood, T.
    Journal: arXiv

Dr. Keiichiro Kuronuma – Nuclear Imaging – Best Researcher Award

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Dr. Keiichiro Kuronuma - Nuclear Imaging - Best Researcher Award

National Hospital Organization Okayama Medical Center - Japan

Author Profile 

SCOPUS

Early academic pursuits 🎓

Keiichiro Kuronuma's academic journey began at nihon university, where he earned both his m.d. and ph.d., laying the foundation for his illustrious career in cardiology. his early studies focused on the intricacies of heart disease, providing him with the knowledge and skills needed to excel in this highly specialized field. his dedication to both medicine and research was evident from the beginning, paving the way for his future successes.

Professional endeavors 🏥

After completing his education, keiichiro embarked on a path of clinical excellence. he trained as a fellow in cardiology at nihon university, further sharpening his expertise. seeking to enhance his skill set in advanced imaging, he pursued a fellowship in advanced cardiac imaging at cedars-sinai medical center under the mentorship of dr. daniel berman. this prestigious training led to roles such as chief physician of cardiology at kawaguchi municipal medical center and assistant professor of cardiology at nihon university. today, he serves as the attending physician of cardiology at the national hospital organization okayama medical center.

Contributions and research focus 🔬

Keiichiro’s work is centered around improving diagnostic techniques in cardiology, with a particular focus on nuclear medicine and cardiovascular imaging. his Nuclear Imaging research delves into the use of advanced cardiac imaging techniques like cardiovascular computed tomography and nuclear imaging to enhance the early detection and treatment of heart diseases. through his research, he has contributed to the development of non-invasive methods that provide crucial insights into cardiovascular health.

Accolades and recognition 🏅

Keiichiro’s dedication to his field has earned him board certifications in multiple specialties, including internal medicine, cardiology, nuclear medicine, cardiovascular Nuclear Imaging computed tomography, and cardiovascular interventional therapeutics. these certifications reflect his extensive expertise and his commitment to both patient care and clinical innovation. his accomplishments in the field have positioned him as a respected figure in cardiology.

Impact and influence 🌍

Keiichiro kuronuma’s work has a significant impact on the field of cardiology, particularly in the realm of diagnostic imaging. his expertise in advanced cardiac imaging techniques has improved the accuracy of cardiovascular diagnoses, leading to better treatment outcomes for patients. his contributions have not only enhanced Nuclear Imaging clinical practice but have also inspired other physicians and researchers to explore innovative methods in cardiac care.

Legacy and future contributions 🔮

As Keiichiro continues his work at the national hospital organization okayama medical center, his legacy in cardiology grows. his dedication to improving diagnostic techniques and his commitment to patient care ensure that his influence will endure. future generations of cardiologists will undoubtedly benefit from his pioneering work in advanced cardiac imaging and his leadership in the field.

Notable Publications 

  • Title: Self-reported exercise activity influences the relationship between coronary computed tomography angiographic finding and mortality
    Authors: Natanzon, S.S., Han, D., Kuronuma, K., Berman, D.S., Rozanski, A.
    Journal: Journal of Cardiovascular Computed Tomography, 2024, 18(4), pp. 327–333
  • Title: Downward myocardial creep during stress PET imaging is inversely associated with mortality
    Authors: Kuronuma, K., Miller, R.J.H., Wei, C.-C., Berman, D.S., Slomka, P.J.
    Journal: European Journal of Nuclear Medicine and Molecular Imaging, 2024, 51(6), pp. 1622–1631
  • Title: Patient-Specific Myocardial Infarction Risk Thresholds from AI-Enabled Coronary Plaque Analysis
    Authors: Miller, R.J.H., Manral, N., Lin, A., Slomka, P.J., Dey, D.
    Journal: Circulation: Cardiovascular Imaging, 2024, e016958
  • Title: Automated Motion Correction for Myocardial Blood Flow Measurements and Diagnostic Performance of 82Rb PET Myocardial Perfusion Imaging
    Authors: Kuronuma, K., Wei, C.-C., Singh, A., Berman, D.S., Slomka, P.J.
    Journal: Journal of Nuclear Medicine, 2024, 65(1), pp. 1–8
  • Title: Cardiac Computed Tomography for Quantification of Myocardial Extracellular Volume Fraction: A Systematic Review and Meta-Analysis
    Authors: Han, D., Lin, A., Kuronuma, K., Berman, D.S., Tamarappoo, B.K.
    Journal: JACC: Cardiovascular Imaging, 2023, 16(10), pp. 1306–1317

Dr. Amina Benabid – Medical image analysis – Best Researcher Award

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Dr. Amina Benabid - Medical image analysis - Best Researcher Award

Zhejiang Normal University - China

Professional Profile

SCOPUS

Early Academic Pursuits

Amina Benabid embarked on her academic journey with a Bachelor's degree in Mathematics from Université Frères Mentouri - Constantine 1, Algeria, which she completed in July 2016. She continued her education with a Master's degree in Mathematics from the same university, focusing on Statistical Tests, and graduated in July 2018. Her academic pursuits culminated in a Doctor of Natural Science in Mathematics from Zhejiang Normal University, China, in June 2022. Her doctoral thesis, supervised by Prof. Dao-Hong Xiang, explored the Convergence Theory of Large Margin Learning.

Professional Endeavors

Dr. Amina Benabid has gained diverse professional experience in both research and academic roles. She served as a Research Assistant at the School of Data Science, City University of Hong Kong, under the supervision of Prof. Ding-Xuan Zhou in July 2019. Subsequently, she held a Medical image analysis Postdoctoral fellowship position at Zhejiang Normal University's College of Mathematical Medicine from August 2022 to August 2024, working under the guidance of Prof. Yuan Jing.

Contributions and Research Focus

Dr. Benabid's research interests are focused on Statistical Learning Theory, Medical Image Analysis, Deep Learning, and Semantic Segmentation. She has made significant contributions to these fields through several publications and ongoing research projects. Her work includes papers Medical image analysis such as "Comparison theorems on large-margin learning" and "CFNet: Cross-scale Fusion Network for Brain Tumor Segmentation on 3D MRI Scans." Her research emphasizes integrating contextual information for enhanced brain tumor classification and developing real-time semantic segmentation models.

Accolades and Recognition

Throughout her career, Dr. Benabid has been recognized for her academic achievements and contributions. She received the Outstanding Award for academic innovation in 2021 and was honored as an Excellent Student in 2022. Her work has been acknowledged at various international conferences, including the 4th International Symposium on Artificial Intelligence for Medical Sciences and the 6th International Symposium on Medical image analysis Image Computing and Digital Medicine.

Impact and Influence

Dr. Amina Benabid's research has had a profound impact on advancing the understanding and application of deep learning techniques in medical image analysis, particularly in the context of brain tumor segmentation. Her contributions to the development of real-time semantic segmentation models using innovative approaches like CFNet and P2AT have set benchmarks in the field.

Legacy and Future Contributions

Looking ahead, Dr. Benabid aims to continue her research endeavors, focusing on further advancements in medical image analysis and deep learning. Her commitment to leveraging artificial intelligence for improving healthcare outcomes underscores her dedication to making significant contributions to the field. Through ongoing research and mentorship, she seeks to inspire future generations of researchers and contribute to transformative innovations in medical imaging technologies.

NOTABLE PUBLICATIONS

P2AT: Pyramid pooling axial transformer for real-time semantic segmentation 2024

LARGE MARGIN UNIFIED MACHINES WITH NON-I.I.D. PROCESS 2022

Comparison theorems on large-margin learning 2021(4)