Explainable AI Technologies for Segmenting 3D Imaging Data
Akronym
XAIS
Bidragets beskrivning
The XAIS project creates interactive medical image segmentation methods using explainable AI systems and XR visualization that advise medical experts on image-based diagnosis and clinical treatment planning. The aim is to integrate the user expertise more concretely into the AI capabilities by enabling an interactive dialogue between the actors. This interactive AI system will be based on probabilistic approximate Bayesian Deep Learning methods. This will be integrated with the XR visualization and interaction for efficient feedback to facilitate the AI system for the volumetric segmentation of tissues. For the XR research a Human-Centered Design approach will be used. Controlled user experiments with medical expert users will be carried out. The expected results will make the medical image analysis methods more accurate and reliable, increasing the medical experts' confidence in the segmentation results and leading to savings in analysis time and avoiding possibly costly mistakes.
Visa merStartår
2022
Slutår
2024
Beviljade finansiering
Rollen i Finlands Akademis konsortium
Övriga parter i konsortiet
Finansiär
Finlands Akademi
Typ av finansiering
Akademiprojekt med särskild inriktning
Övriga uppgifter
Finansieringsbeslutets nummer
345448
Vetenskapsområden
Data- och informationsvetenskap
Forskningsområden
Laskennallinen data-analyysi
Identifierade teman
health care