Upcoming Seminars

Speaker

Dashti Ali, PhD student at Queens University, supervised by Dr. Amber Simpson

Title

Topological Data Analysis for Medical Imaging

Abstract
Reliable and robust feature extraction is a fundamental step in machine learning pipelines for deriving informative representations from medical images. Widely used feature extraction approaches in medical imaging domain are radiomics and convolutional neural networks (CNNs). Radiomic features rely on comparisons at the pixel level and can vary under noise and other clinical parameters. CNNs are computationally intensive and may learn spurious patterns that are not relevant to the target task, such as background noise or texture. Topological data analysis (TDA), a recent advancement based on the mathematical field of algebraic topology, addresses some of these limitations by capturing the structure of data across multiple scales through topological and geometric summaries. TDA has demonstrated promising results across various medical imaging applications. This presentation provides an overview of TDA and its potential in medical image analysis, discusses its integration with existing machine learning techniques, and highlights selected recent research projects.

Presenter Bio
Dashti Ali is currently a PhD candidate in the school of computing at Queens University under the supervision of Dr. Amber Simpson. He achieved his MSc in Scientific Computation from the university of Nottingham, UK. His research focus is on the field of machine learning and topological data analysis on medical imaging domain and particularly cancer image analysis.

Website

Dashti Ali

Timing & Location

UComm Seminar Room 2-108

pizza from 11:30, seminar from noon to 1

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NO SEMINAR - Upper Bound

Speaker

Jiamin He, PhD student at the University of Alberta, supervised by Dr. Martha White

Title

TBA

Abstract
TBA

Presenter Bio
TBA

Timing & Location

UComm Seminar Room 2-108

pizza from 11:30, seminar from noon to 1

Add event to calendar

TBA