ARC Future Fellowships - Grant ID: FT190100197

Funded Activity Summary

Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.

Funded Activity Details

Start Date: 01-04-2020

End Date: 31-03-2024

Funding Scheme: ARC Future Fellowships

Funding Amount: $735,000.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Pattern Recognition and Data Mining | Computer Vision | Image Processing | Artificial Intelligence and Image Processing

ANZSRC Socio-Economic Objective (SEO)

Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in the Biological Sciences | Expanding Knowledge in Technology |

Other Keywords

Artificial Intelligence and Image Processing | Computer Vision | Expanding Knowledge in Technology | Expanding Knowledge in the Biological Sciences | Expanding Knowledge in the Information and Computing Sciences | Image Processing | Pattern Recognition and Data Mining

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