School of Engineering Department of Electronic and Computer Engineering 138 Artificial Intelligence Methods for Medical Videos Supervisor: LI Xiaomeng / ECE Student: LIU Ziming / CS/COMP Course: UROP1100, Fall UROP2100, Spring In this UROP 2100 project, I was assigned tasks on surgical video analysis, focusing on future frame prediction. My work basically consists of reproducing the paper “Surgical Workflow Anticipation Using Instrument Interaction” and finding ideas on improving the baseline by reading other papers. I will present them in two parts in the following sections. Artificial Intelligence Methods for Medical Videos Supervisor: LI Xiaomeng / ECE Student: ZHANG Yuyao / DSCT Course: UROP1100, Spring This semester, since my background knowledge of deep learning was not enough, I just read several papers and learned the basic knowledge. So in this report, I will write a summary of some of the papers and some models. Deep Learning for Whole Heart Segmentation and Disease Diagnosis Supervisor: LI Xiaomeng / ECE Student: KUSUMAWARDHANI Adelia Savitri / SENG Course: UROP1000, Summer Advance medical imaging modalities are utilized to provide information regarding the anatomical structures of the heart, occupying an essential role in the prevention and medication of cardiac diseases. Despite the general outstanding accuracy of Deep Neural Networks (DNNs), medical segmentation data suffer from a shortage of data annotation and scarcity in their pixel categories. We believe combining the semi-supervised method from a previously published paper and implementing the tau norm classifier would help achieve better accuracy and bridge the gap in real-world data collection scenarios. In this paper, we will not only evaluate the performance of the tau norm classifier through comparison but also observe the accuracy attained from different tau values.