HASTE Project

Images contain very rich information, and digital cameras combined with image processing and analysis can detect and quantify a range of patterns and processes. The valuable information is however often sparse, and the ever increasing speed at which data is collected results in data-volumes that exceed the computational resources available.

The HASTE project takes a hierarchical approach to acquisition, analysis, and interpretation of image data. We develop computationally efficient measurements for data description, confidence-driven machine learning for determination of interestingness, and a theory and framework to apply intelligent spatial and temporal information hierarchies, distributing data to computational resources and storage options based on low-level image features.

 

News

Postdoc position: AI methods for large-scale microscopy imaging

We are currently looking for an ambitious, highly motivated Postdoc with a good background in AI and imaging to join the HASTE project. This is a 2-year postdoc position. Assignments include development and application of methods for large-scale analysis of microscopy images using AI / Machine Learning within the framework of the HASTE project. The project …

HASTE project meeting over zoom.

Unfortunately, we didn’t get a chance to enjoy the good food and beautiful environment of the Noor Castle, but we had a productive project meeting anyway. The meeting started with report reading and Salman Toor presenting his docent lecture “Distributed Computing e-Infrastructures (DCI): Challenges and Opportunities for Application Experts and Service Providers”. This was followed …

About

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 approach the challenge of automating scientific discovery in massive spatial and temporal image datasets through three concurrent Aims centered on development of efficient measurements of the degree of informativeness in images (Aim1), machine learning models for automatically selecting the most informative parts of data with quantifiable confidence (Aim2), and smart distributed systems capable of creating, managing and prioritizing between computational and storage e-infrastructure based on information hierarchies (Aim3). The developed methodology will be driven by three challenging demonstrators from industry (AstraZeneca AB and Vironova AB) and from SciLifeLab.

Demonstrators

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.

Project Partners

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.

We are participating in SciLifeLab and the eSSENCE collaboration on eScience

People

Project kickoff at Noor’s castle. From back left: Ola Spjuth, Ida-Marie Sintorn, Alan Sabirsh, Lars Carlsson, Markus Hilscher, Carolina Wählby, Johan Karlsson, Andreas Hellander and Salman Toor.

Principal Investigators

Carolina Wählby (PI), Uppsala University

Andreas Hellander (co-PI), Uppsala University

Ola Spjuth (co-PI), Uppsala University

Senior researchers

Salman Toor, Uppsala University

Postdocs

Markus M. Hilscher, SU

Ben Blamey, UU

PhD Students

Ankit Gupta, UU

Phil Harrison, UU

Håkan Wieslander, UU

Ebba Bergman, UU

Tianru Zhang, UU

Project students

Andrea Behanova, UU

Hongru Zhai, UU

Edvin Lundberg, UU

Andy Ishak, UU

Magnus Larsson, UU

Oliver Stien, UU

Industry partners

Ida-Maria Sintorn, Vironova AB

Alan Sabirsh, AstraZeneca AB

Ola Engkvist, AstraZeneca AB

Collaborators

Mats Nilsson, Stockholm University

Alumni

Ernst Ahlberg Helgee, Astra Zeneca AB

Lars Carlsson, Astra Zeneca, AB

Johan Karlsson, AstraZeneca AB

Lovisa Lugnegård, UU

Niharika Gauraha, UU

Håkan Öhrn, UU

Funding

The HASTE project is funded by the Swedish Foundation for Strategic Research (SSF),  under the call “Big Data and Computational Science”. See the press release here. The publications arising from the project are solely the responsibility of the authors and does not necessarily reflect the views of this agency.