Search Results for author: Aasa Feragen

Found 29 papers, 8 papers with code

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

no code implementations11 Aug 2023 Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans

This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.


Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?

no code implementations9 Aug 2023 Nina Weng, Siavash Bigdeli, Eike Petersen, Aasa Feragen

In this work, we investigate the causes of gender bias in machine learning-based chest X-ray diagnosis.


Are demographically invariant models and representations in medical imaging fair?

no code implementations2 May 2023 Eike Petersen, Enzo Ferrante, Melanie Ganz, Aasa Feragen

Medical imaging models have been shown to encode information about patient demographics such as age, race, and sex in their latent representation, raising concerns about their potential for discrimination.

counterfactual Fairness

An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery

no code implementations11 Apr 2023 Chun Kit Wong, Manxi Lin, Alberto Raheli, Zahra Bashir, Morten Bo Søndergaard Svendsen, Martin Grønnebæk Tolsgaard, Aasa Feragen, Anders Nymark Christensen

Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health.

That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation

no code implementations28 Mar 2023 Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen

Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set.

Image Segmentation Segmentation +1

Removing confounding information from fetal ultrasound images

no code implementations24 Mar 2023 Kamil Mikolaj, Manxi Lin, Zahra Bashir, Morten Bo Søndergaard Svendsen, Martin Tolsgaard, Anders Nymark, Aasa Feragen

In order to utilize the vast amounts of data available in these databases, we develop and validate a series of methods for minimizing the confounding effects of embedded text and calipers on deep learning algorithms designed for ultrasound, using standard plane classification as a test case.

Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty

no code implementations23 Mar 2023 Kilian Zepf, Selma Wanna, Marco Miani, Juston Moore, Jes Frellsen, Søren Hauberg, Aasa Feragen, Frederik Warburg

To ensure robustness to such incorrect segmentations, we propose Laplacian Segmentation Networks (LSN) that jointly model epistemic (model) and aleatoric (data) uncertainty in image segmentation.

Image Segmentation Segmentation +1

On (assessing) the fairness of risk score models

no code implementations17 Feb 2023 Eike Petersen, Melanie Ganz, Sune Hannibal Holm, Aasa Feragen

Further, we address how to assess the fairness of risk score models quantitatively, including a discussion of metric choices and meaningful statistical comparisons between groups.


I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks

no code implementations19 Nov 2022 Manxi Lin, Aasa Feragen, Zahra Bashir, Martin Grønnebæk Tolsgaard, Anders Nymark Christensen

Concept bottleneck models (CBMs) include a bottleneck of human-interpretable concepts providing explainability and intervention during inference by correcting the predicted, intermediate concepts.

Decision Making

DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation

no code implementations23 May 2022 Manxi Lin, Zahra Bashir, Martin Grønnebæk Tolsgaard, Anders Nymark Christensen, Aasa Feragen

We conduct experiments on a challenging multi-class ultrasound scan segmentation dataset as well as a well-known retinal imaging dataset.


Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection

1 code implementation4 Apr 2022 Eike Petersen, Aasa Feragen, Maria Luise da Costa Zemsch, Anders Henriksen, Oskar Eiler Wiese Christensen, Melanie Ganz

Instead, while logistic regression is fully robust to dataset composition, we find that CNN performance is generally improved for both male and female subjects when including more female subjects in the training dataset.

Alzheimer's Disease Detection Binary Classification +4

Spot the Difference: Detection of Topological Changes via Geometric Alignment

1 code implementation NeurIPS 2021 Steffen Czolbe, Aasa Feragen, Oswin Krause

As a first step towards solving such alignment problems, we propose an unsupervised algorithm for the detection of changes in image topology.

Domain Adaptation Optical Flow Estimation +1

Graph2Graph Learning with Conditional Autoregressive Models

no code implementations6 Jun 2021 Guan Wang, Francois Bernard Lauze, Aasa Feragen

For such tasks, the main requirement for intermediate representations of the data is to maintain the structure needed for output, i. e., keeping classes separated or maintaining the order indicated by the regressor.

Graph Classification Graph Learning

Is segmentation uncertainty useful?

1 code implementation30 Mar 2021 Steffen Czolbe, Kasra Arnavaz, Oswin Krause, Aasa Feragen

Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem.

Active Learning Image Segmentation +2

Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track

no code implementations21 May 2019 M. Jorge Cardoso, Aasa Feragen, Ben Glocker, Ender Konukoglu, Ipek Oguz, Gozde Unal, Tom Vercauteren

This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019.

BIG-bench Machine Learning

A Formalization of The Natural Gradient Method for General Similarity Measures

no code implementations24 Feb 2019 Anton Mallasto, Tom Dela Haije, Aasa Feragen

The method uses the Kullback-Leibler divergence, corresponding infinitesimally to the Fisher-Rao metric, which is pulled back to the parameter space of a family of probability distributions.

(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs

1 code implementation10 Feb 2019 Anton Mallasto, Jes Frellsen, Wouter Boomsma, Aasa Feragen

We contribute to the WGAN literature by introducing the family of $(q, p)$-Wasserstein GANs, which allow the use of more general $p$-Wasserstein metrics for $p\geq 1$ in the GAN learning procedure.

Learning from graphs with structural variation

1 code implementation29 Jun 2018 Rune Kok Nielsen, Andreas Nugaard Holm, Aasa Feragen

We study the effect of structural variation in graph data on the predictive performance of graph kernels.

Wrapped Gaussian Process Regression on Riemannian Manifolds

no code implementations CVPR 2018 Anton Mallasto, Aasa Feragen

Gaussian process (GP) regression is a powerful tool in non-parametric regression providing uncertainty estimates.

Gaussian Processes regression

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models

no code implementations23 May 2018 Anton Mallasto, Søren Hauberg, Aasa Feragen

Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an \emph{ambient} Euclidean space.

Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes

no code implementations NeurIPS 2017 Anton Mallasto, Aasa Feragen

We prove uniqueness of the barycenter of a population of GPs, as well as convergence of the metric and the barycenter of their finite-dimensional counterparts.

Gaussian Processes

Geodesic Exponential Kernels: When Curvature and Linearity Conflict

no code implementations CVPR 2015 Aasa Feragen, Francois Lauze, Søren Hauberg

However, we show that for spaces with conditionally negative definite distances the geodesic Laplacian kernel can be generalized while retaining positive definiteness.

Grassmann Averages for Scalable Robust PCA

no code implementations CVPR 2014 Soren Hauberg, Aasa Feragen, Michael J. Black

We exploit that averages can be made robust to formulate the Robust Grassmann Average (RGA) as a form of robust PCA.

Dimensionality Reduction Shadow Removal +1

Scalable kernels for graphs with continuous attributes

no code implementations NeurIPS 2013 Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt

While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity; for instance, the popular shortest path kernel scales as $\mathcal{O}(n^4)$, where $n$ is the number of nodes.

General Classification

Geometric tree kernels: Classification of COPD from airway tree geometry

no code implementations29 Mar 2013 Aasa Feragen, Jens Petersen, Dominik Grimm, Asger Dirksen, Jesper Holst Pedersen, Karsten Borgwardt, Marleen de Bruijne

Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree.

Classification General Classification +1

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