Måns Larsson

Publications (19)
Projects (0)

Showing 19 publications

2024

Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images

E. Tragardh, Johannes Ulén, Olof Enqvist et al

Clinical Physiology and Functional Imaging. Vol. In Press

Journal article

2023

Common carotid segmentation in ¹⁸F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based and manual method

Reza Piri, Yaran Hamakan, Ask Vang et al

Clinical Physiology and Functional Imaging. Vol. 43 (2), p. 71-77

Journal article

2022

AI-based detection and characterization of lung tumors in 18F-FDG-PET/CT including measurements of tumor dimension in CT

P. Borrelli, Måns Larsson, R. Rossi Norrlund et al

European Journal of Nuclear Medicine and Molecular Imaging. Vol. 49 (Suppl 1), p. S620-S620

Other text in scientific journal

2022

Detection of mediastinal lymph node non-small cell lung cancer metastasis based on an Artificial Intelligence reading system of 18f-fdg pet-ct

M. Mila Lopez, P. Fierro Alanis, D. Baquero Velandia et al

European Journal of Nuclear Medicine and Molecular Imaging. Vol. 49 (Suppl 1), p. S622-S622

Other text in scientific journal

2022

“Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in ¹⁸F-sodium fluoride PET/CT scans: Head-to-head comparison

Reza Piri, L. Edenbrandt, Måns Larsson et al

Journal of Nuclear Cardiology. Vol. 29 (5), p. 2531-2539

Journal article

2022

PET/CT imaging of spinal inflammation and microcalcification in patients with low back pain: A pilot study on the quantification by artificial intelligence-based segmentation

Reza Piri, Amalie H. Nøddeskou-Fink, Oke Gerke et al

Clinical Physiology and Functional Imaging. Vol. 42 (4), p. 225-232

Journal article

2021

Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival

Pablo Borrelli, Måns Larsson, Johannes Ulen et al

Clinical Physiology and Functional Imaging. Vol. 41 (1), p. 62-67

Journal article

2021

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

David Molnar, Olof Enqvist, Johannes Ulén et al

Scientific Reports. Vol. 11 (1)

Journal article

2020

Geometric Supervision and Deep Structured Models for Image Segmentation

Måns Larsson

Doctoral thesis

Show project

2019

Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization

Måns Larsson, Erik Stenborg, Carl Toft et al

Proceedings of the IEEE International Conference on Computer Vision (October), p. 31-41

Paper in proceeding

Show project

2019

A cross-season correspondence dataset for robust semantic segmentation

Måns Larsson, Erik Stenborg, Lars Hammarstrand et al

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2019-June, p. 9524-9534

Paper in proceeding

Show project

2018

Revisiting Deep Structured Models for Pixel-Level Labeling with Gradient-Based Inference

Måns Larsson, Anurag Arnab, Shuai Zheng et al

SIAM Journal on Imaging Sciences. Vol. 11 (4), p. 2610-2628

Journal article

2018

A projected gradient descent method for crf inference allowing end-to-end training of arbitrary pairwise potentials

Måns Larsson, Anurag Arnab, Fredrik Kahl et al

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10746, p. 564-579

Paper in proceeding

2018

Robust Abdominal Organ Segmentation Using Regional Convolutional Neural Networks

Måns Larsson, Zhang Yuhang, Fredrik Kahl

Applied Soft Computing Journal. Vol. 70, p. 465-470

Journal article

2018

Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction

Anurag Arnab, Shuai Zheng, Sadeep Jayasumana et al

IEEE Signal Processing Magazine. Vol. 35 (1), p. 37-52

Journal article

2018

End-to-End Learning of Deep Structured Models for Semantic Segmentation

Måns Larsson

Licentiate thesis

2017

Robust abdominal organ segmentation using regional convolutional neural networks

Måns Larsson, Zhang Yuhang, Fredrik Kahl

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10270 LNCS, p. 41-52

Paper in proceeding

2017

Max-margin learning of deep structured models for semantic segmentation

Måns Larsson, Jennifer Alvén, Fredrik Kahl

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10270 LNCS, p. 28-40

Paper in proceeding

2016

Deepseg: Abdominal Organ Segmentation Using Deep Convolutional Neural Networks

Måns Larsson, Yuhang Zhang, Fredrik Kahl

SSBA

Other conference contribution

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