André Pedersen

Brattørkaia 17A

7010 Trondheim

Trøndelag, Norway

Hi! I’m a Senior AI Engineer at DIPS AS. I have strong experience in using artificial intelligence and image analysis techniques for various medical applications, aiming to develop solutions that can assist clinicians in their daily practice. Currently, my main focus is on using generative AI and large multimodal models to solve various problems in the industry.

I was a MSc student at the Arctic University of Norway (UiT), from August 2014 to June 2019, in applied physics and mathematics, specializing in machine learning and statistics. I completed a summer internship at SINTEF Digital in 2018, which I ended up collaborating with on my Master’s thesis. From January 2019, I worked part-time at SINTEF while finishing my degree.

I started my PhD fellowship October 2019 in collaboration with the same research group as for my Master’s thesis. I was a PhD Candidate until 2023, where I published several papers on computational pathology, while I also held a position as a Research Scientist at the Medical Image Analysis group at SINTEF. I successfully defended my PhD November 2024.

In parallel to my PhD work, I have developed open, standalone software for C++ and Python, mainly using Qt5/PySide6 (e.g., FastPathology and Raidionics). I have published open command line tools (e.g., livermask), developed Python packages (e.g., gradient-accumulator, torchstain), and published articles to high-impact scientific journals related to medical image analysis and deep learning (on topics such as image classification, semantic segmentation, image-to-image registration, high-performance computing, semi-supervised learning, and natural language processing).

I have also written a book chapter and acted as a reviewer for scientific journals, such as Medical Image Analysis, Nature Scientific Reports, Frontiers in Medicine, IJCARS, QIMS, and BMC Medical Imaging. Lastly, I have (co-)supervised five Master’s students working on using deep learning for supervised/semi-supervised segmentation of 3D medical images (CT), multilabel histopathology image classification, and bronchoscopy video navigation.

news

Jul 17, 2025	Our work on using immunohistochemistry to develop semantic segmentation models for histopathology was published in PLOS One (see here).
Jul 4, 2025	Our research article exploring evoluationary algorithms for dimensionality reduction to improve WSI classification, got published in Bioengineering (see here).
Jun 9, 2025	Our work on predicting estrogen receptor status from HE-stained breast cancer slides was recently published in Frontiers in Medicine. Available here.
May 20, 2025	Research article presenting DRU-Net for lung cancer classification in whole slide images has been published in the Journal of Imaging. Available here.
May 1, 2025	Started as a Senior AI Engineer at DIPS AS. For announcement, see here.
Jan 17, 2025	Clinical research article published in the Journal of Neurosurgery. Available here.

today's publication

Sci.Rep
Raidionics: an open software for pre-and postoperative central nervous system tumor segmentation and standardized reporting

David Bouget, Demah Alsinan, Valeria Gaitan, Ragnhild Holden Helland, André Pedersen, Ole Solheim, and Ingerid Reinertsen

Scientific Reports Sep 2023

Abs Bib PDF Code Video

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential risks from inherent decisions’ subjectivity. To tackle this problem, the proposed Raidionics open-source software has been developed, offering both a user-friendly graphical user interface and stable processing backend. The software includes preoperative segmentation models for each of the most common tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and metastases), together with one early postoperative glioblastoma segmentation model. Preoperative segmentation performances were quite homogeneous across the four different brain tumor types, with an average Dice around 85% and patient-wise recall and precision around 95%. Postoperatively, performances were lower with an average Dice of 41%. Overall, the generation of a standardized clinical report, including the tumor segmentation and features computation, requires about ten minutes on a regular laptop. The proposed Raidionics software is the first open solution enabling an easy use of state-of-the-art segmentation models for all major tumor types, including preoperative and postsurgical standardized reports.
@article{bouget2023raidionics, abbr = {Sci.Rep}, author = {Bouget, David and Alsinan, Demah and Gaitan, Valeria and Helland, Ragnhild Holden and Pedersen, André and Solheim, Ole and Reinertsen, Ingerid}, year = {2023}, month = sep, pages = {}, title = {Raidionics: an open software for pre-and postoperative central nervous system tumor segmentation and standardized reporting}, volume = {13}, journal = {Scientific Reports}, doi = {10.1038/s41598-023-42048-7}, rgvalue = {16}, bibtex_show = {true}, code = {https://github.com/raidionics/Raidionics}, pdf = {https://arxiv.org/pdf/2305.14351.pdf}, video = {https://www.youtube.com/watch?v=cm9Mxg7Fuj8} }