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

May 12, 2024	Achieved a certification in Machine Learning in Production from DeepLearning.AI. See the certificate here.
Jan 23, 2024	Achieved the Microsoft Azure AI Engineer Associate Certificate. See the certificate here.
Jan 19, 2024	Achieved the Microsoft Azure Data Scientist Associate Certificate. See the certificate here.
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.

today's publication

IEEE-A
High Performance Neural Network Inference, Streaming, and Visualization of Medical Images Using FAST

Erik Smistad, Andreas Østvik, and André Pedersen

IEEE Access Sep 2019

Abs Bib PDF Code Video

Deep convolutional neural networks have quickly become the standard for medical image analysis. Although there are many frameworks focusing on training neural networks, there are few that focus on high performance inference and visualization of medical images. Neural network inference requires an inference engine (IE), and there are currently several IEs available including Intel’s OpenVINO, NVIDIA’s TensorRT, and Google’s TensorFlow which supports multiple backends, including NVIDIA’s cuDNN, AMD’s ROCm and Intel’s MKL-DNN. These IEs only work on specific processors and have completely different application programming interfaces (APIs). In this paper, we presents methods for extending FAST, an open-source high performance framework for medical imaging, to use any IE with a common programming interface. Thereby making it easier for users to deploy and test their neural networks on different processors. This article provides an overview of current IEs and how they can be combined with existing software such as FAST. The methods are demonstrated and evaluated on three performance demanding medical use cases: real-time ultrasound image segmentation, computed tomography (CT) volume segmentation, and patch-wise classification of whole slide microscopy images. Runtime performance was measured on the three use cases with several different IEs and processors. This revealed that the choice of IE and processor can affect performance of medical neural network image analysis considerably. In the most extreme case of processing 171 ultrasound frames, the difference between the fastest and slowest configuration were half a second vs. 24 seconds. For volume processing, using the CPU or the GPU, showed a difference of 2 vs. 53 seconds, and for processing an whole slide microscopy image, the difference was 81 seconds vs. almost 16 minutes. Source code, binary releases and test data can be found online on GitHub at https://github.com/smistad/FAST/.
@article{smistad2019fast, abbr = {IEEE-A}, bibtex_show = {true}, author = {Smistad, Erik and Østvik, Andreas and Pedersen, André}, year = {2019}, month = sep, pages = {1-1}, title = {High Performance Neural Network Inference, Streaming, and Visualization of Medical Images Using FAST}, volume = {PP}, journal = {IEEE Access}, doi = {10.1109/ACCESS.2019.2942441}, pdf = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8844665}, code = {https://github.com/smistad/FAST}, video = {https://www.youtube.com/watch?v=iuevRnZMDgg}, rgvalue = {1} }