The HASTE project takes a holistic approach to new, intelligent ways of processing and managing very large amounts of microscopy images to leverage the imminent explosion of image data from modern experimental setups in the biosciences. One central idea is to represent datasets as intelligently formed and maintained information hierarchies, and to prioritize data acquisition and analysis to certain regions/sections of data based on automatically obtained metrics for usefulness and interestingness. To arrive at such smart systems for scientific discovery in image data, we will pursue a range of topics such as efficient data mining in image data, machine learning models with quantifiable confidence that learn an object’s interestingness, and development of intelligent and efficient cloud systems capable of mapping data and compute to a variety of cloud computing and data storage e-infrastructure based on the quality and interestingness of the data.
We will focus our efforts on microscopy data, and work in three specific areas where image collection results in data volumes difficult to handle with today’s computational resources, namely:
- Large-scale time-lapse experiments exploring the dynamics of cells and drug. delivery particles in collaboration with Astra Zeneca.
- Nanometer-resolution transmission electron microscopy data of in collaboration with Vironova AB.
- Multi-modal digital pathology data from SciLifeLab Sweden.
We expect the resulting methodologies and frameworks to be highly relevant also for other scientific and industrial applications, including surveillance, predictive maintenance and quality control.
The project is a collaboration between the Wählby lab (PI), Hellander lab (co-PI), both at the Department of Information Technology, Uppsala University, the Spjuth lab (co-PI) at the Department of Pharmaceutical Biosciences, Uppsala University, the Nilsson lab at the Department of Biochemistry and Biophysics at Stockholm University and SciLifeLab, Vironova AB and AstraZeneca AB.