Rate My Professor Mingchen Gao

Gao's research centers on big healthcare data, medical imaging informatics, computer vision, and machine learning. He develops algorithms to enhance AI models for medical image analysis, tackling issues such as adaptation to real-world data, handling limited samples for rare diseases, preserving patient privacy, and mitigating catastrophic forgetting in deep learning. Notable achievements include the National Science Foundation CAREER Award of $578,519 for AI-assisted medical imaging diagnostics. Other honors encompass the NIH Fellows Award for Research Excellence (2017), NIH Imaging Sciences Training Program Fellowship (2014-2016), IPMI Scholarship for Junior Researchers (2013), and Best Paper Award at the 6th Conference on Functional Imaging and Modeling of the Heart (2011). Key publications include "Joint solution for PET image segmentation, denoising, and partial volume correction" in Medical Image Analysis (2018), "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning" in IEEE Transactions on Medical Imaging (2016), "Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks" (CMBBE 2016), and "Highly precise partial volume correction for PET images: An iterative approach via shape consistency" in IEEE ISBI (2015, oral). Gao advises PhD and MS students, serves on committees, and reviews for journals such as IEEE Transactions on Medical Imaging and conferences like MICCAI and ISBI. He is recognized as a faculty expert on AI-powered medical imaging techniques.