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.



HASTE Team wins the Adipocyte Cell Imaging Challenge 2020 organised by AstraZeneca and AI Sweden

AI Sweden and AstraZeneca organised the Adipocyte Cell Imaging Challenge, a two-week-long hackathon to help AstraZeneca accelerate the drug development process. The task was to use machine learning in solving the problem of labelling cell images without requiring toxic preprocessing of cell cultures by predicting the content of the fluorescence images from the corresponding bright-field images. Eight teams consisting …

Data Engineer position available in HASTE

We are looking for a skilled Data Engineer to join the HASTE team! Tasks In collaboration with other researchers, develop, implement and test systems for AI-controlled automated microscopes. The task includes interacting directly with the microscope and establishing pipelines where models trained on previously taken images decide and control where the microscope should take images …

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 …


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.


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


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


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

Edvin Lundberg, UU

Andy Ishak, UU

Magnus Larsson, UU

Industry partners

Ida-Maria Sintorn, Vironova AB

Alan Sabirsh, AstraZeneca AB

Ola Engkvist, AstraZeneca AB


Mats Nilsson, Stockholm University


Ernst Ahlberg Helgee, Astra Zeneca AB

Lars Carlsson, Astra Zeneca, AB

Johan Karlsson, AstraZeneca AB

Lovisa Lugnegård, UU

Niharika Gauraha, UU

Håkan Öhrn, UU

Oliver Stien, UU

Hongru Zhai, UU

Andrea Behanova, UU


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.