Lab’s Semester Gathering

The laboratory’s semester gathering at ‘Eatogether’ restaurant. To celebrate the graduation of our seniors and to wish our juniors all the best in their future endeavors. Let’s enjoy our time together to the fullest.

The laboratory’s semester gathering at ‘Eatogether’ restaurant. To celebrate the graduation of our seniors and to wish our juniors all the best in their future endeavors. Let’s enjoy our time together to the fullest.
Dr. Meng-Hsiun Tsai is visiting Johns Hopkins University’s Institute for Clinical and Translational Research (ICTR) as a visiting scholar. During this period, the professor will be conducting research on the comorbidity of diabetes and cancer.
Farewell Party for Fu-Sheng Hsueh, Yu-Sheng Cheng, Min-Yu Chen, Yen-Chih Feng, Wen-Chieh Li, Chia-Hsien Li, and Yen-Chun Lu! Wishing everyone to become extraordinary ordinary individuals on their future life journey.
Congratulations! The laboratory’s industry cooperation project (Directed by Yen-Chih Feng, Chia-Hsien Li, Hao-Chang Kao, and Yun-Hsin Li) has been honored with the Excellence Award in the Poster Competition at Taichung Veterans General Hospital.
Lab conducts a collaborative research project with Taichung Veterans General Hospital to Predict the Brain Tumor Growth. This research utilizes deep learning and machine learning to construct pre-processing techniques for brain tumor MRI images, as well as models for detecting the areas of brain tumors and edema. This facilitates a comparison of the extent of…
Lab conducts a collaborative research project with Taichung Veterans General Hospital to predict brain perfusion region in stroke patients. The project aims to construct pre- and post-operative brain perfusion region prediction models based on deep learning techniques, and conduct quantitative analyses to assist physicians in more accurately assessing patient conditions.
Lab Conducts a collaborative research project with Changhua Christian Hospital to predict the prognosis and healthcare consumption of hospitalized trauma patients. The project introduces deep learning for predicting in-hospital mortality and length of stay for patients. Therefore, it contributes to predicting the prognosis and healthcare consumption of hospitalized trauma patients, ultimately enhancing the quality of…