Medical image analysis

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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

Research group

Code resources

Publications in the research area of medical image analysis

Selected publications

Latest publications

2026

Anders Eklund (2026) Increasing statistical power in functional MRI through permutation and multivariate statistics Cognitive Neuroscience, p. 1-2 (Article in journal) https://dx.doi.org/10.1080/17588928.2026.2682170
Kristin Zeiler, Sofia Morberg Jämterud, F. León, Agnes Andersson, Ulrika Birberg Thornberg, Ida Blystad, Anestis Divanoglou, Anders Eklund, David Engblom, Richard Levi (2026) Affective dimensions of fatigue in post COVID-19 condition: An interdisciplinary investigation across phenomenology and biomedicine Phenomenology and the Cognitive Sciences (Article in journal) https://dx.doi.org/10.1007/s11097-026-10151-5
Sofia Thorell, Janne West, Hanna Lindblom, Mats Hammar, Magnus Borga, Anna-Clara Spetz Holm (2026) Effects of resistance training on postmenopausal women's muscle strength, muscle volume and muscle fat infiltration: A secondary analysis of a randomised controlled trial Maturitas, Vol. 207, Article 108856 (Article in journal) https://dx.doi.org/10.1016/j.maturitas.2026.108856
Christoforos Spyretos, Juan Manuel Pardo Ladino, Hakon Blomstrand, Per Nyman, Oscar Snödahl, Alia Shamikh, Nils Elander, Neda Haj-Hosseini (2026) Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images Journal of Neuropathology and Experimental Neurology (Article in journal) https://dx.doi.org/10.1093/jnen/nlaf163
Iulian Emil Tampu, Per Nyman, Christoforos Spyretos, Ida Blystad, Alia Shamikh, Gabriela Prochazka, Teresita Díaz de Ståhl, Johanna Sandgren, Peter Lundberg, Neda Haj-Hosseini (2026) Pediatric brain tumor classification using digital pathology and deep learning: Evaluation of SOTA methods on a multi-center Swedish cohort Brain Pathology, Vol. 36, Article e70029 (Article in journal) https://dx.doi.org/10.1111/bpa.70029

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