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