Overview

Date

22 September Workshop date

Location

online

Submission deadline

27 June (AOE) Paper submissions due. Now 30 June (AOE)

Submission link

https://easychair.org/cfp/TDA4BIOMEDICALIMAGING

Background

Recent years have witnessed an increasing interest in the role topology plays in machine learning and data science. Topology offers a collection of techniques and tools that have matured to a field known today as Topological Data Analysis (TDA). TDA provides a general and multi-purpose set of robust tools that have shown excellent performance in several real-world applications. These tools are naturally applicable to numerous types of data including images, points cloud, graphs, meshes, time-varying data and more. TDA techniques have been increasingly used with other techniques such as deep learning to increase the performance, and generalizability of a generic learning task. Further, the properties of the topological tools allow discovering complex relationships and separating signals that are hidden in the data from noise. Finally, topological methods naturally lend themselves to visualization rendering them useful for for tasks that require interpretability and explainability.

All these properties of topological-based methods strongly motivate the adoption of TDA tools to various applications and domains including neuroscience, bioscience, biomedicine, and medical imaging. This workshop will focus on using TDA techniques to enhance the performance, generalizability, efficiency, and explainability of the current methods applied to medical data. In particular, the workshop will focus on using TDA tools solely or combined with other computational techniques (e.g., feature engineering and deep learning) to analyze medical data including images/videos, sounds, physiological, texts and sequence data. The combination of TDA and other computational approaches is more effective in summarizing, analyzing, quantifying, and visualizing complex medical data. This workshop will bring together mathematicians, biomedical engineers, computer scientists, and medical doctors for the purpose of showing the strength of using TDA-based tools for medical data analysis.

If you have any question please email us at: tda.miccai.workshop@gmail.com