March15 2023, 11:30 am, EC4-2101A

*** 11:30am Presenter:  Navid Shahsavari ***

Title: Enhancing Thermocular’s Performance for Clinical Applications

In my upcoming seminar, I will outline the primary goals and recent progress of the “ThermOcular” project. This innovative effort is focused on a dual visual-thermal camera system designed to non-invasively measure and track temperatures across the ocular surface through thermal imaging. My work aims to boost the ThermOcular system’s performance by improving its ability to segment ocular components accurately, enhancing the precision and efficiency of image registration processes, and creating a software system designed for clinicians. This software enables the examination of patients and analysis of ocular surface temperatures, ensuring that the ThermOcular device can be effectively integrated into real-world clinical settings.

*** 12:00pm Presenter:  Rishav Bhardwaj ***

Title: New method to improve the diagnostic utility of OCTA images in retinal disease

There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to  random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images.