Yucheng Tang

Email:

yuchengt at nvidia dot com

tangyc1183 at gmail dot com

Office:

Nvidia Redmond/Kirkland:

11431 Willows Rd NE #200

Redmond, WA 98052

I am a research scientist of medical and healthcare division at Nvidia. I work on a wide range of challenges in computational and efficient healthcare AI including efficient medical image computing, ultra-high resolution image analysis, data-driven deep learning and their translational research to clinical, computational biomedicine, and AI infrastructure.

My Ph.D. research at Department of Computer Science and Electrical Engineering of Vanderbilt University focus was medical image analysis, computational biomedicine and disease progressions. My Ph.D supervisor is Professor Bennett A. Landman. Medical-image Analysis and Statistical Interpretation lab (MASI) performs translational research to explore innovative, clinically useful techniques. I spent great time at Nvidia (CA) and Siemens Healthineers (Princeton, New Jersey). I was a member of Vanderbilt Institute of Surgery Engineering. I served as independent instructor in the department of computer science at Vanderbilt teaching courses CS1104 and DS8001.

We have broad projects of medical AI at Nvidia and Vanderbilt University. Medical Open Network for AI (MONAI) is one of our major projects, our goal is to build computing platforms and infrastructures for innovative machine learning in medical image analysis across multiple fields: radiology, pothology, endoscopy and more. Contact me or the team if you are interested.

[Scholar] [LinkedIn]

What's new

Updates on my research and expository publications, discussion of open chanllenges, and other topics.

03/2024 GTC 2024 ! We released Nvidia Healthcare's latest cutting-edge computational AI infrastructure: MONAI Cloud Service at Nvidia GTC24, along with our foundational CT segmentation models, VISTA3D and VISTA2D

03/2024 Two challenges and workshops (BodyAtlas and KPIS) are accepted at MICCAI 2024, we will release the leading radiological and pathological datasets, continue to contribute useful and impactful efforts to the community.

02/2024 One paper on body atlas is accepted at ICLR 2024

01/2024 PrPSeg , our universal pathology segmentation solution to appear at CVPR 2024

12/2023 AbdomenAtlas-8K , our leading CT benchmark dataset for Human Body Atlas is released and accepted to NeurIPS 2023

11/2023 Human in the loop annotation with body atlas benchmark. Large Language-Image Model for Multi-Organ Segmentation. Transitioning to Fully-Supervised Pre-Training with Large-Scale Radiology ImageNet, 3 papers (Oral) to present at RSNA 2023

10/2023 CLIP Universal Model , our state-of-the-art solution for CT segmentation is accepted to ICCV 2023

10/2023 Our 3D Constrastive Segmentation is accepted to IEEE-JBHI

10/2023 MONAI Label , our Nvidia teams' creation of AI annotation infrastructure, is published and accepted to Medical Image Analysis

08/2023 Whole brain MRI segmentation (133 structures) in 4s! Our first ever solution UNesT is accepted to Medical Image Analysis

06/2023 3 papers accepted to MICCAI 2023. Our works on histopathology image analysis, segmentation, body compisitions analysis to appear at Vancouver, MICCAI23!

03/2023 6 papers oral presentation to appear at SPIE 2023

02/2023 Our review paper on Transforming medical imaging with Transformers? is accepted to Journal Medical Image Analysis.

10/2022 Two new papers accepted to MICCAI 2022.

09/2022 Eight new papers accepted to SPIE 2023.

04/2022 Our paper on kidney tissue atlas and correspondence is accepted to Journal of Computers in Biology and Medicine.

04/2022 We are organizing ICML 2022 workshop on Interpretable Machine Learning in Healthcare ICML-IMLH 2022.

02/2022 Our paper on self-supervised pre-training with Swin UNETR for medical segmentation is accepted to CVPR'22.

10/2021 Our collaborative paper on machine learning assited inverse design of Tamm plasmon thermal emitters is accepted to Nature Material.

10/2021 Seven papers accepted to SPIE 2022.

08/2021 One paper on transformer-based medical image segmentation model (UNETR) is accepted to WACV 2022.

05/2021 Two papers accepted to MICCAI 2021.

03/2021 Our paper on body part regression with self-supervision is accepted to Transactions of Medical Imaging.

02/2021 Our paper on validation of CT biomarker on splenomegaly is accepted to Journal of Medical Imaging.

02/2021 Our paper on 3D high-resolution organ segmentation is accepted to Medical Image Analysis.

01/2021 Our paper on CT Contrast Phase Detection using GANs is accepted to Medical Physics.

01/2021 One paper accepted to ISBI 2021.

01/2021 Our paper on how longitudinal modeling improves lung cancer prediction is accepted to Medical Image Analysis.

Before 2021

Thomas S. Huang 04/2020

I was greatly saddened to learn that Thomas S. Huang passed away, aged 84.

My own experience have no direct relation to professor Huang, but I have known many of his students in a large scale of areas. Professor Huang is known for his kindness, professional research in computer vision.

Professor Huang is a lighthouse in both academia and industry of computer vision, an idle for many young professionals. He was arguably an extreme point. He will very much be missed

04/2020 Our paper on adaptive learning for abdominal organ segmentation is accepted to Journal of Medical Imaging.

04/2020 Our paper on multi-path recurrent network is accepted to Neurocomputing.