简介：博士毕业于澳大利亚新南威尔士大学，目前在上海交通大学计算机系任副研究员、硕导，任新南威尔士大学联席讲师（Adjunct Lecturer）。在S3 Graphics，AMD和CSIRO等机构有多年的商业经验。目前主要的研究方向为病理图像的智能分析诊断及大规模图像的高性能并行计算。在医学图像分析领域以一作或通讯在MedIA、TMI、MICCAI等期刊会议发表论文三十篇；在高性能并行计算领域会议如IPDPS、MICRO、DAC等发表论文十篇。出版著作“通用图形处理器设计-GPGPU编程模型与架构原理”（清华大学出版社）。主持国家自然科学基金青年项目，上海市自然科学基金面上项目，以及两项“交大之星“计划医工交叉研究基金；并于国家知识产权局授权专利四项。担任MICS、CSIG、CAAI等学会专委，Frontiers in Radiology 的主题编委。
Jing Ke is currently an associate professor in the Computer Science Department at Shanghai Jiao Tong University. She graduated from University of New South Wales as a Ph.D. She has been conferred the academic title of Adjunct Lecturer at the University of New South Wales and also serves as a Technical Consultant at the Shanghai Artificial Intelligence Laboratory. Jing’s main academic focus is computer-assisted pathology image analysis. More than thirty papers are published in journals and conferences with high reputation in the medical imaging domain, such as IEEE Transactions on Medical Imaging (TMI), Medical Image Analysis (MedIA), IEEE J-BHI, MICCAI, etc., most as first or last author. She has more than six years of industrial experience at AMD (Advanced Micro Devices) and VIA Technologies, the world's leading GPU graphics processor research and development companies. She has also published over ten papers in conferences on architecture and parallel computing, e.g., MICRO, DAC and IPDPS, as well as a textbook on GPU architecture analysis.She has been granted a NSFC Young Scientist Fund, a Shanghai General Program General Program, and a couple of fundings from Shanghai Jiao Tong University.
Email: kejing@cs.sjtu.edu.cn
Academic
Activities
✨26/7/2023 Delivered a lecture at the Cambridge Mathematics of Information in Healthcare Hub at the Centre for Mathematical Sciences on "Annotation and Practical Problems in Deep Learning based Computational Pathology".
✨16/7/2023 Gave a keynote speech at the 10th Medical Imaging Computing Seminar (MICS) on "Research on Pathological Image Annotation and Clinical Application Problems Based on Deep Learning".
✨03/7/2023 Participated in the 2nd Shanghai Jiao Tong University Artificial Intelligence Exhibition, presenting a video exhibition on "Efficient Diagnosis of Thyroid Cytopathology Images by Artificial Intelligence".
✨24/6/2023 Had 4 papers accepted at the 2023 Medical Image Computing and Computer-Assisted Intervention(MICCAI).
Publications
Journal Articles
1) J Ke, K Liu, Y Sun, Y Xue, J Huang, Y Lu, J Dai, Y Chen, X Han, Y Shen, D Shen. Artifact Detection and Restoration in Histology Images with Stain-Style and Structural Preservation. IEEE Transactions on Medical Imaging (TMI).
2) J Ke, Y Lu, Y Shen, J Zhu, Y Zhou, J Huang, J Yao, X Liang, Y Guo, Z Wei, S Liu, Q Huang, F Jiang & D Shen. A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets. Medical Image Analysis (MedIA), 2023, 85(102758).
3) Y Li, Y Shen, J Zhang, S Song, Z Li, J Ke, D Shen. A Hierarchical Graph V-Net with Semi-supervised Pre-training for Histological Image based Breast Cancer Classification. IEEE Transactions on Medical Imaging (TMI).
4) J Ke, Y Shen, Y Lu, Y Guo & D Shen. Mine Local Homogeneous Representation by Interaction Information Clustering with Unsupervised Learning in Histopathology Images. Computer Methods and Programs in Biomedicine.
5) Y Shen, A Sowmya, Y Luo, X Liang, D Shen & J Ke. A Federated Learning System for Histopathology Image Analysis with an Orchestral Stain-Normalization GAN, IEEE Transactions on Medical Imaging (TMI).
6) Y Shen, D Shen & J Ke. Identify Representative Samples by Conditional Random Field of Cancer Histology Images, IEEE Transactions on Medical Imaging (TMI), Volume: 41, Issue: 12, December 2022.
7) Y Shen & J Ke. Sampling Based Tumor Recognition in Whole-slide Histology Image with Deep Learning Approaches. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume: 19, Issue: 4, 2022, pp: 2431 – 2441.
8) J Ke, Y Shen, Y Lu, J Deng, J. D. Wright, Y Zhang, Q Huang, D Wang, N Jing, X Liang & F Jiang. Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning, Laboratory Investigation 101, pp: 513–524, 2021.
9) M Liu, Y Liu, P Xu, H Cui, J Ke & J Ma. Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis using Histopathological Images. IEEE Transactions on Medical Imaging(TMI).
10) C Wang, S Li, J Ke, C Zhang & Y Shen. RandStainNA++: Enhance Random Stain Augmentation and Normalization through Foreground and Background Differentiation. IEEE Journal of Biomedical and Health Informatics(JBHI).
Conference Articles
1) J Wang, F Ino & J Ke. PRF: A Fast Parallel Relaxed Flooding Algorithm for Voronoi Diagram Generation on GPU. 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2023.
2) J Zhu, Y Shen, H Zhang & J Ke. An Anti-Biased TBSRTC-Category Aware Nuclei Segmentation Framework with A Multi-Label Thyroid Cytology Benchmark, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
3) E L Carbonell, Y Shen, X Yang & J Ke. COVID-19 Pneumonia Classification with Transformer from Incomplete Modalities. MICCAI 2023.
4) Y Shen & J Ke. StainDiff: Transfer Stain Styles of Histology Images with Denoising Diffusion Probabilistic Models and Self-Ensemble. MICCAI 2023.
5) Y Shen, Y Luo, D Shen & J Ke. RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization, MICCAI 2022.
6) J Ke, Y Shen, X Liang, D Shen. Contrastive Learning Based Stain Normalization Across Multiple Tumor Histopathology. MICCAI 2021.
7) Y Shen & J Ke. Su-Sampling Based Active Learning For Large-Scale Histopathology Image. International Conference on Image Processing (ICIP), 2021.
8) J Huang, Y Shen, D Shen & J Ke. CA2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection. MICCAI 2021.
9) Y Shen, Y Lu, Y Luo & J Ke. Cluster Image Patches with Multiple Mutual Information in Unlabelled Whole-Slide Image. International Conference on Bioinformatics and Biomedicine (BIBM) 2021
10) Z Song, F Wu, X Liu, J Ke, N Jing & X Liang. VR-DANN: Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration, the 53rd IEEE/ACM International Symposium on Microarchitecture (MICRO) 2020.
11) X Song, J Wang, T Li, L Jiang, J Ke, X Liang & N Jing. GPNPU: Enabling Efficient Hardware-Based Direct Convolution with Multi-Precision Support in GPU Tensor Cores. the 57th ACM/IEEE Design Automation Conference (DAC) 2020.
12) Y Shen & J Ke. A Deformable CRF Model for Histopathology Whole-slide Image Classification. MICCAI 2020.
13) J Ke, Y Shen, Jason D. Wright, N Jing, X Liang & D Shen. Identifying patch-level MSI from histological images of Colorectal Cancer by a Knowledge Distillation Model. BIBM 2020
14) J Ke, Yiqing Shen, Yi Guo & Xiaoyao Liang. Fast Tumor Detector in Whole-slide Image with Dynamic Programming Based Monte Carlo Sampling, International Conference on Image Processing (ICIP), 2020.
Books
景乃锋，柯晶，梁晓峣。通用图形处理器设计-GPGPU编程模型与架构原理，清华大学出版社，2022-05出版
Project
Funding
国家自然科学基金青年：弱标注肿瘤病理图像分析及异构系统高通量计算关键技术研究 2022.1～2024.12
上海市自然科学基金面上项目：基于临床癌症辅助诊断的病理图像空间关联性特征融合研究 2023.4～2026.3
上海交通大学“交大之星“计划医工交叉研究基金：2023.1～2025.12
a）基于人工智能的多模态影像构建肝癌门脉癌栓的新型精准分型
b）基于深度学习的多模态多切面超声及病理图像分析早期评估乳腺癌新辅助化疗疗效
Patents
第一发明人
细胞病理图像分割方法及装置； ZL202011379134.0；授权公告日， 2022-11-01
病理图像染色风格归一化方法及装置；ZL202010912039.6；授权公告日：2022-11-01
图像分类方法、装置、电子设备和存储介质；ZL202110510967.4；授权公告日：2021-5-11
Alumni
Achievements
上海交通大学-卫宁健康智慧医疗全国挑战赛（2023年第六届）
• 三维全视野Ca诊断-前瞻奖
上海交通大学-卫宁健康智慧医疗全国挑战赛（2022年第五届）
• 微世界放大镜队-非凡奖（高校最高奖）
• 计算者联盟-精英奖
大学生创新创业训练计划 （大创）
国家级优秀项目：
• 深度学习自动检测医学图像中的瑕疵噪声（2023 二十五期）
• 基于深度学习的医学影像实时诊断系统（2023二十五期）
• 运用全局标签的弱监督学习计算分析甲状腺病理图像（2022二十二期）
上海市级优秀项目：
• 基于神经网络的病理组织学图像中生物标记的识别（2021 二十一期）
• 基于自监督学习的医学影像分析（2022 二十三期）
莙政项目
• 2023年第十一期：王嘉怡；基于深度学习的甲状腺分级预测可视化系统
• 2022年第十期：周艺晋；基于深度学习的精准医疗肿瘤预后模型开发与实验研究
• 2020年第八期（获校优秀导师称号）：沈逸卿；基于深度学习的全视野数字切片的分类模型何自动标注策略