André Pedersen

Forsker

Enhet:: SINTEF Digital
Avdeling:: Helse
Kontorsted:: Trondheim

I'm a Research Scientist at SINTEF Health Research and PhD Candidate at the Department of Clinical and Molecular Medicine at NTNU. I'm an open-source advocate, with keen interest in accelerating research and development by making all my research and implementations as public and easily interpretable as possible. See my personal website for more information (https://andreped.github.io/) or my GitHub page (https://github.com/andreped).

Utdanning

- 2019-2023: On-going PhD Candidate in Medical Technology at NTNU.
- 2014-2019: MSc in Applied Physics and Mathematics (Sivilingeniør), specialized in Machine Learning and Statistics from UiT.

Kompetanse og fagområder

My research include machine learning, image analysis, software development, and computational statistics in the field of medicine. The data types range from 3D MRI images, gigapixel histopathological images, bronchoscopy videos, free text, and recently mobile sensor data.

https://www.linkedin.com/in/andré-pedersen-7166a4162/?originalSubdomain=no

ResearchGate

https://www.researchgate.net/profile/Andre-Pedersen?ev=hdr_xprf

ORCID

https://orcid.org/0000-0002-3637-953X

Fremhevede publikasjoner

2023 Vitenskapelig artikkel
Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation
2022 Vitenskapelig artikkel
Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting Les publikasjonen
2022 Vitenskapelig artikkel
Teacher-student approach for lung tumor segmentation from mixed-supervised datasets Les publikasjonen
2021 Vitenskapelig artikkel
FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology Les publikasjonen
2019 Vitenskapelig artikkel
High performance neural network inference, streaming, and visualization of medical images using FAST Les publikasjonen

Andre publikasjoner

2023 Vitenskapelig artikkel
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting
2022 Vitenskapelig artikkel
H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images Les publikasjonen
2022 Faglig kapittel
Artificial Intelligence in Studies of Malignant Tumours
2022 Vitenskapelig artikkel
Train smarter, not harder: learning deep abdominal CT registration on scarce data
2022 Poster
Raidionics: automatic pre- and post-operative brain tumor segmentation and reporting
2022 Vitenskapelig artikkel
Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology Les publikasjonen
2022 Vitenskapelig artikkel
Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding Les publikasjonen
2021 Vitenskapelig artikkel
Meningioma Segmentation in T1-Weighted MRI Leveraging Global Context and Attention Mechanisms Les publikasjonen
2021 Vitenskapelig Kapittel/Artikkel/Konferanseartikkel
Preliminary Processing and Analysis of an Adverse Event Dataset for Detecting Sepsis-Related Events Les publikasjonen
2021 Vitenskapelig artikkel
Fast meningioma segmentation in T1-weighted magnetic resonance imaging volumes using a lightweight 3D deep learning architecture

Kontaktinformasjon

Besøksadresse:
Professor Brochs gate 2
Trondheim

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