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Neda Haj Hosseini

Associate Professor, Docent

I am an associate professor in biomedical engineering at the Department of Biomedical Engineering (IMT). My research focus is on innovative multimodal engineering solutions for cancer diagnosis and treatment.

Beating Cancer: Innovative Engineering Solutions for Cancer and Oncology Diagnostics and Treatment

The burden of cancer can be reduced by prevention, early detection and appropriate treatment with innovative technologies playing a major role in the procedures. Most of the techniques, however, require trained clinical specialists for interpretation and diagnosis that can be an obstacle for implementation on a large scale. The ongoing artificial intelligence (AI) revolution has tackled previously unsolvable analytic tasks and enabled automatic data interpretation that could address this challenge.


List of publications at Scopus

List of publications at Google Scholar


Barncancerfonden stödjer ny medicinsk teknik som kan ge bättre diagnos och effektivare behandling | Barncancerfonden

Joanna Cocozzas stiftelse 2022

About me



Iulian Emil Tampu, Anders Eklund, Kenth Johansson, Oliver Gimm, Neda Haj-Hosseini (2023) Diseased thyroid tissue classification in OCT images using deep learning: towards surgical decision support Journal of Biophotonics, Vol. 16, Article e202200227 Continue to DOI
Iulian Emil Tampu, Neda Haj-Hosseini, Ida Blystad, Anders Eklund (2023) Deep learning-based detection and identification of brain tumor biomarkers in quantitative MR-images Machine Learning: Science and Technology, Vol. 4, Article 035038 Continue to DOI
Christoforos Spyretos, Iulian Emil Tampu, Anders Eklund, Neda Haj-Hosseini (2023) Classification of Brain Tumour Tissue in Histopathology Images Using Deep Learning
Peter Milos, Neda Haj-Hosseini, Jan Hillman, Karin Wårdell (2023) 5-ALA fluorescence in randomly selected pediatric brain tumors assessed by spectroscopy and surgical microscope Acta Neurochirurgica, Vol. 165, p. 71-81 Continue to DOI


Neda Haj-Hosseini, Hanna Jonasson, Magnus Stridsman, Lars Carlsson (2022) Virtuell laboration för undervisning av elektrisk säkerhet inom medicinteknik
Iulian Emil Tampu, Anders Eklund, Neda Haj-Hosseini (2022) Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images Scientific Data, Vol. 9, Article 580 Continue to DOI
Tamara Bianchessi, Iulian Emil Tampu, Anders Eklund, Neda Haj-Hosseini (2022) Classification of pediatric brain tumors based on MR-images using deep learning
Neda Haj-Hosseini, Joakim Lindblad, Bengt Hasséus, Vinay Vijaya Kumar, Narayana Subramaniam, Jan-Michaél Hirsch (2022) Early Detection of Oral Potentially Malignant Disorders: A Review on Prospective Screening Methods with Regard to Global Challenges Journal of Maxillofacial and Oral Surgery Continue to DOI
Iulian Emil Tampu, Ida Blystad, Neda Haj-Hosseini, Anders Eklund (2022) Deep-learning based brain tumor segmentation using quantitative MRI
Iulian Emil Tampu, Anders Eklund, Kenth Johansson, Oliver Gimm, Neda Haj-Hosseini (2022) Classification of thyroid diseases in OCT images using convolutional neural networks Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX, Article 11949-23


Johan Richter, Neda Haj-Hosseini, Peter Milos, Martin Hallbeck, Karin Wårdell (2021) Optical Brain Biopsy with a Fluorescence and Vessel Tracing Probe Operative Neurosurgery, Vol. 21, p. 217-224 Continue to DOI
Jan-Michael Hirsch, Neda Haj-Hosseini, Carina Krüger Weiner, Bengt Hasséus, Joakim Lindblad (2021) Icke-invasiv kontroll av cellförändringar i munslemhinnan Tandläkartidningen, Vol. 9, p. 48-55
Iulian Emil Tampu, Neda Haj-Hosseini, Anders Eklund (2021) Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation? Diagnostics, Vol. 11, Article 1159 Continue to DOI
Iulian Emil Tampu, Anders Eklund, Neda Haj-Hosseini (2021) Deep-learning for thyroid microstructure segmentation in 2D OCT images Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV Continue to DOI

Previous bachelor's and master's theses


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