School of Engineering Department of Computer Science and Engineering 102 Deep Learning for Medical Image Analysis Supervisor: CHEN Hao / CSE Student: LAU Ying Yee Ava / DSCT Course: UROP2100, Fall This project aims to analyse the health conditions of the patella of patients, so as to determine the type of surgery, either total knee arthroplasty (TKA) or unicompartmental knee arthroplasty (UKA), is required. Given slices of 2D binary segmentation masks of the patella, it is reconstructed into a 3D triangular mesh, then several descriptors are computed based on the features of the mesh. These descriptors are later inputted to the logistic regression model, which in the end provides a probability for having TKA or UKA. The predictive ability of the logistic regression model is examined: it could provide predictions with accuracy around 80% when compared to the ground truth. Deep Learning for Medical Image Analysis Supervisor: CHEN Hao / CSE Student: WIOGO Calvin / SENG Course: UROP1000, Summer The Coronavirus disease (Covid-19) has resulted in millions of deaths and is still posing a considerable risk to the public’s well-being, both physically and mentally. The development of automatic segmentation has mitigated this situation by enabling medical professionals to perform screening, treatment planning, and follow-up assessments effectively on infected patients. This research paper proposes TransUNet, a method of processing Two-Dimensional CT images for Covid-19 segmentation. TransUNet comprises U-Net (a form of Convoluted Neural Network) and transformer. U-Net has become prevalent in the medical field for medical image segmentation and its implementation has shown great success on multiple medical occasions, namely Magnetic Resonance (MR) Cardiac Segmentation and Computed Tomography (CT) Organ Segmentation. The transformer employs a self-attention mechanism to model long-range relations, overcoming the limitations of the Convolution Neural Network having local sensitivity.