André Pedersen

Brattørkaia 17A

7010 Trondheim

Trøndelag, Norway

Hi! I’m a Senior Machine Learning Engineer at Sopra Steria. 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.

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.

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

Jan 7, 2024	Achieved a certification in Generative AI with Large Language Models from DeepLearning.AI. See the certificate here.
Jan 6, 2024	Achieved the Microsoft Azure Data Fundamentals Certificate. See the certificate here.
Dec 20, 2023	Paper published in Neuro-Oncology Advances on growth analysis of untreated meningiomas, see here.
Nov 2, 2023	Paper published in Nature Scientific Reports on segmentation of glioblastomas in early post-operative multi-modal MRI, see here.
Oct 12, 2023	Achieved the Microsoft Azure AI Fundamentals Certificate. See the certificate here.
Oct 2, 2023	Started as a Senior ML Engineer at Sopra Steria. For announcement, see here.

today's publication

IEEE-A
FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology

André Pedersen, Marit Valla, Anna Bofin, Javier Frutos, Ingerid Reinertsen, and Erik Smistad

IEEE Access Apr 2021

Abs Bib PDF Code Video

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.
@article{pedersen2021fastpathology, abbr = {IEEE-A}, bibtex_show = {true}, author = {Pedersen, André and Valla, Marit and Bofin, Anna and Pérez de Frutos, Javier and Reinertsen, Ingerid and Smistad, Erik}, year = {2021}, month = apr, pages = {1-1}, title = {FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology}, volume = {PP}, journal = {IEEE Access}, doi = {10.1109/ACCESS.2021.3072231}, selected = {true}, pdf = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9399433}, code = {https://github.com/AICAN-Research/FAST-Pathology}, video = {https://www.youtube.com/watch?v=1s7jU6T7S3U}, rgvalue = {4} }