The objective of this joint project is to develop and validate automated software for the generation and analysis of collateral imaging that can be readily used in clinics to aid in improving diagnostic accuracy and efficiency for ischemic stroke. The role of QuIIL is to develop AI software for collateral imaging.
The objective of this international joint project is to develop and deploy a cloud-based AI tool MitProfiler for pathology that can be readily used in a practical setting across multiple institutions and for multiple purposes. The role of QuIIL is to develop and evaluate a domain invariant AI model.
The objective of this international joint project is to conduct co-design of a neural processing unit (NPU) architecture and a light-weight AI model for an intelligent edge device. The role of QuIIL is to develop a hardware resource-aware light-weight AI model.
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 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.
The main objective of this international collaboration research project is to develop unsupervised learning-based artificial intelligence for the analysis of heterogeneous datasets.