Accepted papers
- An Artificial Fish Swarm Algorithm for Identifying Associations between Multiple Variants and Multiple Phenotypes by Ruoyu Liu; Xin Lai; Xiao Xiao; Xuanping Zhang; Xiaoyan Zhu; Jiayin Wang
- Comparing digital histology slides with multiple staining based on decoloring and dyeing technique by Chunbao Wang; Zhe Yang; Kai Wang; Pargorn Puttapirat; Chen Li; Guanjun Zhang
- A Robust Approach to Locate HER2 and CEN17 Signals in Varied FISH Images by Jiasheng Liu; Juan Liu; Yuqi Chen; Chunbing Hua; Zhuoyu Li; Zhiqun Zuo
- Effects of annotation granularity in deep learning models for histopathological images by Jiangbo Shi; Zeyu Gao; Haichuan Zhang; Pargorn Puttapirat; Chunbao Wang; Xiangrong Zhang; Chen Li
- OpenHI2 — Open source histopathological image platform by Pargorn Puttapirat, Haichuan Zhang, Jingyi Deng, Yuxin Dong, Jiangbo Shi, Hongyu He, Zeyu Gao, Chunbao Wang, Xiangrong Zhang, and Chen Li
Introduction to workshop
The artificial intelligence techniques in pathology have gained attentions, especially when the quantity and accessibility of digitized histopathological images and the other forms of data were exponentially increasing in the past decade. However, the potential of the technology is far from being fully exploited in pathology. Except that the accuracy and generality of the algorithms and models are continuously improving, applications also need specialized well-established semantic standards and analysis methods to really meet the criteria of assisting pathologists. Therefore, it is important to promote the computational understanding-mechanisms in pathology, which include image analysis and more complicated multi-modal methods. The mechanisms should also consist of data management, establishing semantic standards and medically validating the intelligent systems to guarantee the medical usability and enhance the meaningfulness. The works may greatly benefit pathologists and eventually patients, leading to accurate diagnosis and effective treatments. This workshop is a forum for researchers to discuss new ideas, findings, best practices, and techniques to build up works in pathology, complementing the conventional diagnosis with information science and technology.
Research topics included in the workshop
- Data acquisition for computational analysis
- Data management, e.g. data infrastructure and digital standards/formatting in pathology
- Image analysis in pathology, e.g. whole-slide image (WSI) and tissue microarray (TMA) analysis
- Intelligent systems for cancer screening, diagnosis, and prognosis
- Analysis techniques enhancing pathology methods
- Medical validation of intelligent systems
- Application of AI-based systems in clinical cancer practice
- Semantic resources for pathology, e.g. ontologies and data dictionaries
- Automated pathology report generation
- Multimodal data analysis in pathology, e.g. integrating radiology scans, electronic medical records, omics data, etc.
- Quantitative pathology
Important Dates
Sep 20, 2019: Due date for full workshop papers submission
Oct 15, 2019: Notification of paper acceptance to authors
Nov 1, 2019: Camera-ready of accepted papers
Nov 20th, 2019: Artificial Intelligence in Pathology Workshop - 10:30am-12:30pm, Venue: Encore1
Submission
Please submit a full-length paper (up to 8 page IEEE 2-column format) through the online submission system. Please follow the format instruction.
Electronic submissions (in PDF or Postscript format) are required. Selected participants will be asked to submit their revised papers in a format to be specified at the time of acceptance.
Program Chairs or co-chairs
Chen Li – Xi’an Jiaotong University, China (chair)
Lixia Yao – Mayo Clinic, US
Ruifeng Guo – Mayo Clinic, US
Program Committee Members
Guanjun Zhang – The 1st Affiliated Hospital of Xi’an Jiaotong University, China
Hui Guo – The 1st Affiliated Hospital of Xi’an Jiaotong University, China
Rui Jiang – Tsinghua University, China
Sumitra Thongprasert - Chiang Mai University, Thailand
Weiguo Zhu – Peking Union Medical College Hospital, China
Xiangrong Zhang – Xidian University, China
Xuchao Zhang – Guangdong General Hospital, China