EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System
The Cancer Registry of Norway (CRN) collects information on cancer patients by receiving cancer messages from different medical entities (e.g., medical labs, hospitals) in Norway. Such messages are validated by an automated cancer registry system: GURI. Its correct operation is crucial since it lays the foundation for cancer research and provides critical cancer-related statistics to its stakeholders. Constructing a cyber-cyber digital twin (CCDT) for GURI can facilitate various experiments and advanced analyses of the operational state of GURI without requiring intensive interactions with the real system. However, GURI constantly evolves due to novel medical diagnostics and treatment, technological advances, etc. Accordingly, CCDT should evolve as well to synchronize with GURI. A key challenge of achieving such synchronization is that evolving CCDT needs abundant data labelled by the new GURI. To tackle this challenge, we propose EvoCLINICAL, which considers the CCDT developed for the previous version of GURI as the pretrained model and fine-tunes it with the dataset labelled by querying a new GURI version. EvoCLINICAL employs a genetic algorithm to select an optimal subset of cancer messages from a candidate dataset and query GURI with it. We evaluate EvoCLINICAL on three evolution processes. The precision, recall, and F1 score are all greater than 91%, demonstrating the effectiveness of EvoCLINICAL. Furthermore, we replace the active learning part of EvoCLINICAL with random selection to study the contribution of transfer learning to the overall performance of EvoCLINICAL. Results show that employing active learning in EvoCLINICAL increases its performances consistently.
Thu 7 DecDisplayed time zone: Pacific Time (US & Canada) change
11:00 - 12:30 | Machine Learning IVResearch Papers / Ideas, Visions and Reflections / Industry Papers at Golden Gate C2 Chair(s): Diptikalyan Saha IBM Research India | ||
11:00 15mTalk | Dynamic Data Fault Localization for Deep Neural Networks Research Papers Yining Yin Nanjing University, China, Yang Feng Nanjing University, Shihao Weng Nanjing University, Zixi Liu Nanjing University, Yuan Yao Nanjing University, Yichi Zhang Nanjing University, Zhihong Zhao , Zhenyu Chen Nanjing University Media Attached | ||
11:15 15mTalk | Assisting Static Analysis with Large Language Models: A ChatGPT Experiment Ideas, Visions and Reflections Haonan Li University of California at Riverside, USA, Yu Hao University of California at Riverside, USA, Yizhuo Zhai University of California at Riverside, USA, Zhiyun Qian University of California at Riverside, USA Media Attached | ||
11:30 15mTalk | Understanding the Bug Characteristics and Fix Strategies of Federated Learning Systems Research Papers Xiaohu Du Huazhong University of Science and Technology, Xiao CHEN Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Jialun Cao Hong Kong University of Science and Technology, Ming Wen Huazhong University of Science and Technology, Shing-Chi Cheung Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hai Jin Huazhong University of Science and Technology Media Attached | ||
11:45 15mTalk | EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System Industry Papers Chengjie Lu Simula Research Laboratory; University of Oslo, Xu Qinghua Simula Research Laboratory; University of Oslo, Tao Yue Beihang University, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University, Thomas Schwitalla Cancer Registry of Norway, Jan F. Nygård Cancer Registry of Norway DOI Media Attached | ||
12:00 15mTalk | Learning Program Semantics for Vulnerability Detection via Vulnerability-specific Inter-procedural Slicing Research Papers bozhi wu Singapore Management University, Shangqing Liu Nanyang Technological University, Yang Xiao Institute of Information Engineering at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Zhiming Li Nanyang Technological University, Singapore, Jun Sun Singapore Management University, Shang-Wei Lin Nanyang Technological University Media Attached | ||
12:15 15mTalk | [Remote] DeepRover: A Query-efficient Blackbox Attack for Deep Neural Networks Research Papers Fuyuan Zhang Kyushu University, Xinwen Hu Hunan Normal University, Lei Ma The University of Tokyo / University of Alberta, Jianjun Zhao Kyushu University Media Attached | ||