On-going Projects

Domain-Invariant AI for Robust Image Analysis

In collabolation with Prof. Nasir Rajopoot at University of Warwick, UK

The main objective of this project is to develop an AI-based image analysis framework that can resolve the domain-shift problem in computational pathology and is widely applicable without facing performance degradation.

  • Conduct an independent and cross-domain validation of AI algorithms to investigate the effect of domain-shift on pathology image analysis
  • Develop a framework for domain-invariant tissue representations and test the framework for the two AI algorithms at both ends
  • Develop a web interface to collect external histology images and to construct an enhanced dataset for testing and validation of the AI algorithms’ robustness

Order Learning Laboratory: AI for Comparative Data Analysis and Assessment

This is a joint project led by Prof. Chang-su Kim. The role of QuIIL is to investigate and apply a new machine learning paradigm, i.e., order learning, on medical imaging domains.

  • Develop a ranking-based framework to integrate hierarchy of datasets into AI models
  • Develop an order learning-based AI models for an improved decision-making in disease diagnosis

AI for Multi-scale Multi-level Graph Analysis

This projects aims at developing multi-scale multi-level graph techniques for an effective and efficient analysis of high-resolution, large-scale digital pathology images that can lead to an improved learning capability of AI and precise disease diagnosis.

  • Design a multi-scale feature processing block to extract and utilize the global and local context
  • Develop a patch sampling framework for an effective construction of a graph netowkr in large-scale images
  • Develop a multi-scale multi-level graph neural network to improve decision making in large-scale images
  • Develop a knowledge distillation technique for a graph neural network

Unsupervised AI for Heterogeneous Medical Data

In collabolation with Prof. Purang Abolmaesumi at University of British Columbia (UBC), Canada

The main objective of this international collaboration research project is to develop unsupervised learning-based artificial intelligence for the analysis of heterogeneous datasets.

  • Construct a multi-modal, heterogeneous database to develop unsupervsied AI models
  • Develop an unsupervised feature learning framework, including data sampling techniques and objective functions, to explore and utilize unlabeled datasets
  • Evaluate the proposed unsupervsied AI models in comparison to the up-to-date supervised and unsupervised models