Search Results for author: Marleen de Bruijne

Found 52 papers, 10 papers with code

Leveraging point annotations in segmentation learning with boundary loss

no code implementations6 Nov 2023 Eva Breznik, Hoel Kervadec, Filip Malmberg, Joel Kullberg, Håkan Ahlström, Marleen de Bruijne, Robin Strand

Hence it is intuitively inappropriate for weak supervision, where the ground truth label may be much smaller than the actual object and a certain amount of false positives (w. r. t.

Computational Efficiency Heart Segmentation +3

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, Horst Joachim Mayer, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Isabell Tributsch, 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, Marina Camacho, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, 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.

Fairness

Source Identification: A Self-Supervision Task for Dense Prediction

no code implementations5 Jul 2023 Shuai Chen, Subhradeep Kayal, Marleen de Bruijne

The paradigm of self-supervision focuses on representation learning from raw data without the need of labor-consuming annotations, which is the main bottleneck of current data-driven methods.

blind source separation Brain Tumor Segmentation +5

On the dice loss gradient and the ways to mimic it

no code implementations9 Apr 2023 Hoel Kervadec, Marleen de Bruijne

In the past few years, in the context of fully-supervised semantic segmentation, several losses -- such as cross-entropy and dice -- have emerged as de facto standards to supervise neural networks.

Semantic Segmentation

Why is the winner the best?

no code implementations CVPR 2023 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin Van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Huan Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Płotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem Abdul Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.

Benchmarking Multi-Task Learning

Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation

1 code implementation14 Sep 2022 Shuai Chen, Antonio Garcia-Uceda, Jiahang Su, Gijs van Tulder, Lennard Wolff, Theo van Walsum, Marleen de Bruijne

In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure.

Computed Tomography (CT) Image Segmentation +3

A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

no code implementations22 Sep 2021 Robin Camarasa, Daniel Bos, Jeroen Hendrikse, Paul Nederkoorn, M. Eline Kooi, Aad van der Lugt, Marleen de Bruijne

This paper highlights a systematic approach to define and quantitatively compare those methods in two different contexts: class-specific epistemic uncertainty maps (one value per image, voxel and class) and combined epistemic uncertainty maps (one value per image and voxel).

Automated Segmentation and Volume Measurement of Intracranial Carotid Artery Calcification on Non-Contrast CT

no code implementations20 Jul 2021 Gerda Bortsova, Daniel Bos, Florian Dubost, Meike W. Vernooij, M. Kamran Ikram, Gijs van Tulder, Marleen de Bruijne

To evaluate the method, we compared manual and automatic assessment (computed using ten-fold cross-validation) with respect to 1) the agreement with an independent observer's assessment (available in a random subset of 47 scans); 2) the accuracy in delineating ICAC as judged via blinded visual comparison by an expert; 3) the association with first stroke incidence from the scan date until 2012.

Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images

no code implementations31 Mar 2021 Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne

Previous studies have shown that it is possible to adversarially manipulate automated segmentations produced by neural networks in a targeted manner in the white-box attack setting.

Semantic Segmentation

Crowdsourcing Airway Annotations in Chest Computed Tomography Images

1 code implementation20 Nov 2020 Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne

We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall.

Computed Tomography (CT)

Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track

no code implementations29 Jun 2020 Tal Arbel, Ismail Ben Ayed, Marleen de Bruijne, Maxime Descoteaux, Herve Lombaert, Chris Pal

This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9 July 2020.

BIG-bench Machine Learning

Region-of-interest guided Supervoxel Inpainting for Self-supervision

1 code implementation26 Jun 2020 Subhradeep Kayal, Shuai Chen, Marleen de Bruijne

Self-supervised learning has proven to be invaluable in making best use of all of the available data in biomedical image segmentation.

Image Inpainting Image Segmentation +2

DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data

3 code implementations18 Jun 2020 Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nürnberger

The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance.

Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images

no code implementations24 Apr 2020 Subhradeep Kayal, Florian Dubost, Harm A. W. M. Tiddens, Marleen de Bruijne

Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present.

Data Augmentation Texture Classification

When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans

no code implementations12 Apr 2020 Oliver Werner, Kimberlin M. H. van Wijnen, Wiro J. Niessen, Marius de Groot, Meike W. Vernooij, Florian Dubost, Marleen de Bruijne

We showed that networks optimized using only weak labels reflecting WMH volume generalized better for WMH volume prediction than networks optimized with voxel-wise segmentations of WMH.

Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations

no code implementations4 Nov 2019 Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne

In this paper, we propose a novel semi-supervised method that, in addition to supervised learning on labeled training images, learns to predict segmentations consistent under a given class of transformations on both labeled and unlabeled images.

Image Segmentation Segmentation +2

A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs

no code implementations22 Aug 2019 Antonio Garcia-Uceda Juarez, Raghavendra Selvan, Zaigham Saghir, Marleen de Bruijne

In this approach, the convolutional layers at the deepest level of the UNet are replaced by a GNN-based module with a series of graph convolutions.

3D Medical Imaging Segmentation Graph Neural Network

Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network

no code implementations29 Jul 2019 Kimberlin M. H. van Wijnen, Florian Dubost, Pinar Yilmaz, M. Arfan Ikram, Wiro J. Niessen, Hieab Adams, Meike W. Vernooij, Marleen de Bruijne

We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans.

Lesion Detection

Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation

1 code implementation29 Jul 2019 Shuai Chen, Gerda Bortsova, Antonio Garcia-Uceda Juarez, Gijs van Tulder, Marleen de Bruijne

The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes.

Image Segmentation Medical Image Segmentation +2

Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks

no code implementations5 Jun 2019 Florian Dubost, Hieab Adams, Pinar Yilmaz, Gerda Bortsova, Gijs van Tulder, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne

For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective.

object-detection regression +1

A cross-center smoothness prior for variational Bayesian brain tissue segmentation

1 code implementation11 Mar 2019 Wouter M. Kouw, Silas N. Ørting, Jens Petersen, Kim S. Pedersen, Marleen de Bruijne

Here we present a smoothness prior that is fit to segmentations produced at another medical center.

Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

no code implementations8 Mar 2019 Vikram Venkatraghavan, Florian Dubost, Esther E. Bron, Wiro J. Niessen, Marleen de Bruijne, Stefan Klein

In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions.

Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks

1 code implementation21 Nov 2018 Raghavendra Selvan, Thomas Kipf, Max Welling, Antonio Garcia-Uceda Juarez, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne

Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications.

An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images

no code implementations8 Nov 2018 Shuai Chen, Marleen de Bruijne

Fully-connected Conditional Random Field (CRF) is often used as post-processing to refine voxel classification results by encouraging spatial coherence.

General Classification Semantic Segmentation

Learning to quantify emphysema extent: What labels do we need?

no code implementations17 Oct 2018 Silas Nyboe Ørting, Jens Petersen, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne

We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels.

BIG-bench Machine Learning Multiple Instance Learning

Deep Learning from Label Proportions for Emphysema Quantification

no code implementations23 Jul 2018 Gerda Bortsova, Florian Dubost, Silas Ørting, Ioannis Katramados, Laurens Hogeweg, Laura Thomsen, Mathilde Wille, Marleen de Bruijne

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue.

Hydranet: Data Augmentation for Regression Neural Networks

no code implementations12 Jul 2018 Florian Dubost, Gerda Bortsova, Hieab Adams, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne

The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0. 73 on the first task and 0. 84 on the second task, only using between 25 and 30 scans with a single global label per scan for training.

Data Augmentation regression

Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses

no code implementations23 Jun 2018 Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne

We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters.

Segmentation

Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans

no code implementations19 Jun 2018 Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne

We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets.

Extraction of Airways using Graph Neural Networks

no code implementations12 Apr 2018 Raghavendra Selvan, Thomas Kipf, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne

We present extraction of tree structures, such as airways, from image data as a graph refinement task.

Decoder

Mean Field Network based Graph Refinement with application to Airway Tree Extraction

no code implementations10 Apr 2018 Raghavendra Selvan, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne

Performance of the method is compared with two methods: the first uses probability images from a trained voxel classifier with region growing, which is similar to one of the best performing methods at EXACT'09 airway challenge, and the second method is based on Bayesian smoothing on these probability images.

Bayesian Inference

Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing

no code implementations7 Aug 2017 Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne

The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector.

Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images

no code implementations7 Jun 2017 Isabel Pino Peña, Veronika Cheplygina, Sofia Paschaloudi, Morten Vuust, Jesper Carl, Ulla Møller Weinreich, Lasse Riis Østergaard, Marleen de Bruijne

The classifier has a stronger correlation with PFT than the density based method, the percentage of emphysema in the intersection of annotations from both radiologists, and the percentage of emphysema annotated by one of the radiologists.

Multiple Instance Learning

Early Experiences with Crowdsourcing Airway Annotations in Chest CT

no code implementations7 Jun 2017 Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne

Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually.

Computed Tomography (CT)

GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network

no code implementations22 May 2017 Florian Dubost, Gerda Bortsova, Hieab Adams, Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne

We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count.

Lesion Detection regression

Label Stability in Multiple Instance Learning

no code implementations15 Mar 2017 Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog

We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers.

Multiple Instance Learning

Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners

no code implementations15 Mar 2017 Veronika Cheplygina, Annegreet van Opbroek, M. Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne

We show that the asymmetry can indeed be informative, and that computing the similarity from the test image to the training images is more appropriate than the opposite direction.

Clustering Lesion Segmentation +2

Extraction of airway trees using multiple hypothesis tracking and template matching

no code implementations24 Nov 2016 Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne

The results show improvements in performance when compared to the original method and region growing on intensity images.

Template Matching

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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