Photo of Gabriel Eilertsen

Gabriel Eilertsen

Assistant Professor, Docent

My research focuses on machine learning for understanding, manipulation and creation of images, with applications in computer graphics and medical image diagnosis.

Deep learning and images

I have a PhD in computer graphics and image processing, with focus on high dynamic range (HDR) imaging and machine learning. I am currently an AI/MLX Assistant Professor within the Wallenberg AI, Autonomous Systems and Software Program (WASP). I am a core member of the Analytic Imaging Diagnostics Arena (AIDA), which is a national arena for research and innovation around AI for medical imaging diagnostics. I am an affiliated researcher of the Center for Medical Image Science and Visualization (CMIV).

My research is focused on machine learning, and particularly by means of deep learning, for computer vision and image processing. The research covers both applied problems within computer graphics and medical diagnosis, as well as more fundamental problems in image generation and data-centric deep learning. Examples of current research projects include:

Synthetic images for machine learning

We explore different methods for generating synthetic images for training of deep neural networks. This includes both conventional methods within computer graphics as well as deep generative modeling, and different combinations of these two (such as neural rendering). We investigate techniques for data-centric machine learning using synthetic data, for augmentation, anonymization, and testing. I currently have a CENIIT project on generative deep learning for data-centric medical imaging. For information, see the project page.

High dynamic range imaging

I have a background in high dynamic range (HDR) imaging, with projects related to tone-mapping, compression, evaluation, and deep learning-based HDR image reconstruction. Recent work include evaluation of methods for deep single-image HDR reconstruction. We show how unreliable existing evaluation protocols are, and suggest techniques for improved correlation between objective metrics and perceptual experiments.



Milda Pocevičiūtė, Gabriel Eilertsen, Stina Garvin, Claes Lundström (2023) Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part V, p. 157-167 Continue to DOI
Alex Knutsson, Jakob Unnebäck, Daniel Jönsson, Gabriel Eilertsen (2023) CDF-Based Importance Sampling and Visualization for Neural Network Training Eurographics Workshop on Visual Computing for Biology and Medicine Continue to DOI
Milda Poceviciute, Gabriel Eilertsen, Claes Lundström (2023) Spatial uncertainty aggregation for false negatives detection in breast cancer metastases segmentation MEDICAL IMAGING 2023, Article 124710W Continue to DOI


Karin Stacke, Jonas Unger, Claes Lundström, Gabriel Eilertsen (2022) Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications The Journal of Machine Learning for Biomedical Imaging, Vol. 1, Article 023
Param Hanji, Rafal K. Mantiuk, Gabriel Eilertsen, Saghi Hajisharif, Jonas Unger (2022) Comparison of single image HDR reconstruction methods - the caveats of quality assessment ACM SIGGRAPH ’22 Conference Proceedings Continue to DOI