Medical image analysis

Gradient background from teal to light blue with hexagonal shapes forming a network pattern. Contains icons for a microscope, lungs, brain, DNA, and medical notes.

Our research group is dedicated to advancing medical image analysis through state-of-the-art machine and deep learning methods.

We focus on expanding the understanding of how deep learning technologies can be applied to medical image analysis and we develop cutting-edge solutions that improve the diagnosis, treatment and thereby patient outcomes. Our medical focus areas are cancer, orthopedics and body composition analysis using multiple image and data modalities.

The mission

We aim to develop new knowledge, implement and evaluate on AI advances for medical image applications and translate these into clinically useful tools that benefit the healthcare and the patients.

Research projects

Photo of a microscope and two close ups of tissue.

Optical Coherence Tomography (OCT)

In this project, OCT鈥檚 potentials for intra-operative and postoperative (laboratory) pathology diagnostics are investigated.

Pragmatic Research in Orthopedics - PRIO

Pragmatic Research in Orthopedics is a research group that aim to optimize the quality of care within the field of orthopedic surgery. We have a strong focus on the orthopedic interventions aimed at the lower extremities.

Research group

Anders Eklund

Senior Associate Professor

Magnus Borga

Deputy Dean, Professor

Neda Haj Hosseini

Associate Professor, Docent

Christoforos Spyretos

PhD student

Iulian Emil Tampu

Principal Research Engineer

Code resources

Publications in the research area of medical image analysis

Selected publications

Cover of publication 'Deep learning for medical image analysis in cancer diagnosis'

Iulian Emil Tampu (2024)

Cover of publication 'Modern multimodal methods in brain MRI'

David Abramian (2023)

Cover of publication 'A path along deep learning for medical image analysis: With focus on burn wounds and brain tumors'

Marco Domenico Cirillo (2021)

Latest publications

2025

Alfredo Ordinola, David Abramian, Magnus Herberthson, Anders Eklund, Evren Özarslan (2025) Scientific Reports, Vol. 15, Article 6580 (Article in journal)

Humaira Batool, Asmat Mukhtar, Sajid Gul Khawaja, Norah Saleh Alghamdi, Asad Mansoor Khan, Adil Qayyum, Ruqqayia Adil, Zawar Khan, Muhammad Usman Akram, Muhammad Usman Akbar, Anders Eklund (2025) IEEE Access, p. 1-1 (Article in journal)

Muhammad Usman Akbar, Wuhao Wang, Anders Eklund (2025) Machine Learning: Science and Technology (Article in journal)

Iulian Emil Tampu, Per Nyman, Christoforos Spyretos, Ida Blystad, Alia Shamikh, Gabriela Prochazka, Teresita Díaz de Ståhl, Johanna Sandgren, Peter Lundberg, Neda Haj-Hosseini (2025) Brain Pathology, Article e70029 (Article in journal)

Iulian Emil Tampu, Tamara Bianchessi, Ida Blystad, Peter Lundberg, Per Nyman, Anders Eklund, Neda Haj-Hosseini (2025) Neuro-Oncology Advances, Vol. 7, Article vdae205 (Article in journal)

Master theses in medical image analysis

Pardo Ladino, Juan Manuel, (2024)
Ardalan, Aydin. (2024)
Evaldsson, Benjamin. (2024)
Spyretos, Christoforos. (2023)
Qiqi, Kristos. (2023)
Ledberg, Rasmus. (2023)
Alva Nilsson, Oliver Andlid, (2022)

News

The image consists of two parts: four sections views of a brain on the one side and fous sections of another brain on the other side

26 April 2024

Synthetic images can save a lot of time in healthcare

AI makes it possible to quickly and accurately mark areas to be eliminated with radiation when treating cancerous tumors. But due to lack of medical data to train AI models with, attempts are underway to train models with synthetic medical images.

Info graphics with illustrations showing how local updates are being sent from the hospitals to a cloud. The cloud sends a new global modell to the hospitals. The trained model gives personal healthcare.

16 April 2024

Developing medical AI without sharing sensitive information

Sharing sensitive data, such as images of brain tumor patients, is complicated. This makes development of AI models for medical image analysis challenging. LiU researchers shows how such models can be trained without sharing sensitive information.

Education and courses

Biomedical Image Science

We develop methods & techniques for depicting anatomy & function in the human body & its organs. From imaging techniques, through methods of analysis and quantification of image information, to validation of new methods and image-based biomarkers.

Cancer

Cancer is a research network for scientists and health care professionals at 福利姬 and at the County Council of Östergötland (Region Östergötland). The research is related to cancer diagnosis, pathogenesis and treatment.

Two scientists are sitting infront of an MRI

Center for Medical Image Science and Visualization, CMIV

CMIV conducts focused front-line research providing solutions to tomorrow鈥檚 clinical issues. The CMIV mission is to develop future methods and tools for image analysis and visualization for applications within health care and medical research.

AIDA

Analytic Imaging Diagnostics Arena (AIDA) is a national arena for research and innovation on analytic image-based diagnostics.

Organisation

Department of Biomedical Engineering (IMT)

A national centre for research, PhD studies and graduate studies within biomedical engineering, since 1972.

福利姬