PhD Student, ECE, Johns Hopkins University Email:ke_zhang20@fudan.edu.cn Github | Linkedin | Google Scholar Research interests: Computer Vision, AI in Healthcare, Medical Image Analysis Ke Zhang is a ECE PhD student at Johns Hopkins University, advised by Prof. Vishal Patel. Previously, she obtained her Master degree from ZMIC Lab at School of Data Science, Fudan University, advised by Prof. Xiahai Zhuang. She obtained her B.S. from the School of Data Science, Fudan University. She interned at Microsoft Research, Sensetime, and PAII Inc.

One paper early accepted by MICCAI 2024 (^ ^).

Selected Publications

ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentation Ke Zhang, Vishal M. Patel MICCAI (early accept), 2024

ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation Ke Zhang, Xiahai Zhuang Arxiv preprint, 2023, paper

ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation Ke Zhang, Xiahai Zhuang MICCAI, 2022, paper and code

CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision Ke Zhang, Xiahai Zhuang CVPR, 2022, paper and code

Deep Compatible Learning for Partially-Supervised Medical Image Segmentation Ke Zhang, Xiahai Zhuang Arxiv preprint, 2021, paper

Journal Reviewer

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

• Medical Image Analysis (MedIA)

• IEEE Transactions on Medical Imaging (TMI)

Conference Reviewer

• International Conference on Machine Learning (ICML)

• Conference on Neural Information Processing Systems (NeurIPS)

• International Conference on Learning Representations (ICLR)

• Computer Vision and Pattern Recognition Conference (CVPR)

• International Conference on Computer Vision (ICCV)

• European Conference on Computer Vision (ECCV)

• International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

Award

• Academic Star Award of Fudan University, 2023

• Excellent Graduate Award of Shanghai, 2023

• China National Scholarship, 2022

Teaching Assistant

• FUDAN DATA130043, Spring 2023, Medical Image Processing.

• FUDAN DATA130044, Autumn 2022, Multivariate Statistical Analysis.

• FUDAN DATA130043, Spring 2022, Medical Image Processing.

• FUDAN DATA130049, Autumn 2020, Image Processing and Visualization.

• FUDAN DATA620018, Spring 2020, Data Visualization.

Internships

• I am lucky to have been able to intern in several excellent institutions. I have worked on data analysis, empirical study, back-end development, etc.

• Now I focus on the research of medical imaging and feel glad to be engaged in the challenging field of AI in healthcare.