Software Defined Network

The primary goal of this project is to establish a complete hospital-based liver cancer database, profiles for data feature extraction, and develop different cancer, prediction models.

This project integrates clinical physical and mental medical practice, medical engineering, information engineering and social welfare units, adopts cross-fields and uses AI technology, and builds an innovative artificial intelligence auxiliary evaluation and treatment system for dementia care based on cultural context. Establish an early intervention mechanism for early detection of dementia, and establish measures to improve cognitive function, emotional state, social and daily behavioral functions.

We have proposed in this project the first time series classification technique that considers accuracy, earliness, and varied lengths simultaneously, containing a novel deep reinforcement learning framework and a new multi-objective optimization neural network algorithm. The proposed technique is fit for the problem of early classification of cardiovascular diseases based on ECG signals and shown to deliver the best performance in this area, holding the leading position worldwide.

Development of AI Platform for Smart Drone - Intelligent Flight: Due to its high mobility and the ability to fly in the sky, the drone has inspired more and more innovative applications/services in recent years. The goal of this project is to resolve the problem of blindly flying an unmanned aerial vehicle (UAV, which a drone in our case) when it is out of human sight or the range of wireless communication, and three major research and development directions will be considered in this project. Three artificial intelligence (AI) technologies, namely, smart sensing, smart control, and smart simulation, are applied in this project. Smart sensing - a flight system is developed, which can avoid the obstacles, complete a flight mission, and land safely. Smart control - an intelligence flight control system and a light-weighted somatosensory vest are developed. Smart simulation - a cost-effective training system and a 3D model simplification method are designed.

PCA-BM includes two models: “Automatic BMs Segmentation AI Model” and " Distant Brain Failure Prediction Model". The former one uses C2FNAS (coarse to fine network architecture search) to detect the location, size, and the number of brain metastases. The latter uses radiomics to extract numerous radiographic features and employs machine learning methods such as XGBoost to establish a prognostic model of brain metastases. PCA-BM provides more precision treatment decisions for patients and improves personalized and accurate overall stereotactic radiosurgery planning.