no code implementations • 14 Nov 2024 • Soumick Chatterjee, Hendrik Mattern, Marc Dörner, Alessandro Sciarra, Florian Dubost, Hannes Schnurre, Rupali Khatun, Chun-Chih Yu, Tsung-Lin Hsieh, Yi-Shan Tsai, Yi-Zeng Fang, Yung-Ching Yang, Juinn-Dar Huang, Marshall Xu, Siyu Liu, Fernanda L. Ribeiro, Saskia Bollmann, Karthikesh Varma Chintalapati, Chethan Mysuru Radhakrishna, Sri Chandana Hudukula Ram Kumara, Raviteja Sutrave, Abdul Qayyum, Moona Mazher, Imran Razzak, Cristobal Rodero, Steven Niederren, Fengming Lin, Yan Xia, Jiacheng Wang, Riyu Qiu, Liansheng Wang, Arya Yazdan Panah, Rosana El Jurdi, Guanghui Fu, Janan Arslan, Ghislain Vaillant, Romain Valabregue, Didier Dormont, Bruno Stankoff, Olivier Colliot, Luisa Vargas, Isai Daniel Chacón, Ioannis Pitsiorlas, Pablo Arbeláez, Maria A. Zuluaga, Stefanie Schreiber, Oliver Speck, Andreas Nürnberger
The human brain receives nutrients and oxygen through an intricate network of blood vessels.
1 code implementation • 26 Jun 2024 • Colton Stearns, Adam Harley, Mikaela Uy, Florian Dubost, Federico Tombari, Gordon Wetzstein, Leonidas Guibas
We evaluate on the Nvidia Dynamic Scenes dataset and the DyCheck iPhone dataset, and show that Gaussian Marbles significantly outperforms other Gaussian baselines in quality, and is on-par with non-Gaussian representations, all while maintaining the efficiency, compositionality, editability, and tracking benefits of Gaussians.
no code implementations • 5 Apr 2024 • Yang Zheng, Qingqing Zhao, Guandao Yang, Wang Yifan, Donglai Xiang, Florian Dubost, Dmitry Lagun, Thabo Beeler, Federico Tombari, Leonidas Guibas, Gordon Wetzstein
This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop.
no code implementations • 21 Oct 2023 • Erfan Darzi, Florian Dubost, Nanna. M. Sijtsema, P. M. A van Ooijen
In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks.
no code implementations • 10 Oct 2023 • Erfan Darzi, Florian Dubost, N. M. Sijtsema, P. M. A van Ooijen
Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks.
1 code implementation • 30 Aug 2023 • Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine de Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
1 code implementation • 18 Oct 2022 • Ali Mirzazadeh, Florian Dubost, Maxwell Pike, Krish Maniar, Max Zuo, Christopher Lee-Messer, Daniel Rubin
We propose an unsupervised fine-tuning method that optimizes the consistency of attention maps and show that it improves both classification performance and the quality of attention maps.
no code implementations • 15 Aug 2022 • Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost, Hieab Adams, David Atkinson, Frederik Barkhof, Mahlet A. Birhanu, Esther E. Bron, Robin Camarasa, Nish Chaturvedi, Yuan Chen, Zihao Chen, Shuai Chen, Qi Dou, Tavia Evans, Ivan Ezhov, Haojun Gao, Marta Girones Sanguesa, Juan Domingo Gispert, Beatriz Gomez Anson, Alun D. Hughes, M. Arfan Ikram, Silvia Ingala, H. Rolf Jaeger, Florian Kofler, Hugo J. Kuijf, Denis Kutnar, Minho Lee, Bo Li, Luigi Lorenzini, Bjoern Menze, Jose Luis Molinuevo, Yiwei Pan, Elodie Puybareau, Rafael Rehwald, Ruisheng Su, Pengcheng Shi, Lorna Smith, Therese Tillin, Guillaume Tochon, Helene Urien, Bas H. M. van der Velden, Isabelle F. van der Velpen, Benedikt Wiestler, Frank J. Wolters, Pinar Yilmaz, Marius de Groot, Meike W. Vernooij, Marleen de Bruijne
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
1 code implementation • 10 Jun 2022 • Soumick Chatterjee, Hadya Yassin, Florian Dubost, Andreas Nürnberger, Oliver Speck
The models are trained by using the input image and only the classification labels as ground-truth in a supervised fashion - without using any information about the location of the region of interest (i. e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels.
no code implementations • 28 Nov 2021 • Siddharth Sharma, Florian Dubost, Christopher Lee-Messer, Daniel Rubin
We evaluate an ImageNet pre-trained Mask R-CNN, a standard deep learning model for object detection, on the task of patient detection using our own curated dataset of 45 videos of hospital patients.
no code implementations • 20 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.
no code implementations • 3 Jun 2021 • Florian Dubost, Erin Hong, Max Pike, Siddharth Sharma, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel Rubin
Optimization plays a key role in the training of deep neural networks.
1 code implementation • ICLR 2022 • Siyi Tang, Jared A. Dunnmon, Khaled Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel L. Rubin, Christopher Lee-Messer
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment.
no code implementations • 31 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.
1 code implementation • 28 Nov 2020 • Florian Dubost, Erin Hong, Nandita Bhaskhar, Siyi Tang, Daniel Rubin, Christopher Lee-Messer
We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times.
4 code implementations • 18 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.
1 code implementation • 11 Jun 2020 • Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein, Florian Dubost, Ioannis Katramados, Laurens Hogeweg, Bart Liefers, Bram van Ginneken, Josien P. W. Pluim, Mitko Veta, Clara I. Sánchez, Marleen de Bruijne
Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model.
no code implementations • 24 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.
no code implementations • 12 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.
1 code implementation • 4 Nov 2019 • Florian Dubost, Benjamin Collery, Antonin Renaudier, Axel Roc, Nicolas Posocco, Gerda Bortsova, Wiro Niessen, Marleen de Bruijne
For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles.
no code implementations • 4 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.
no code implementations • 19 Sep 2019 • Chaoping Zhang, Florian Dubost, Marleen de Bruijne, Stefan Klein, Dirk H. J. Poot
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions.
no code implementations • 29 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.
no code implementations • 17 Jul 2019 • Bernhard Egger, Markus D. Schirmer, Florian Dubost, Marco J. Nardin, Natalia S. Rost, Polina Golland
We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis.
1 code implementation • 1 Jul 2019 • Florian Dubost, Marleen de Bruijne, Marco Nardin, Adrian V. Dalca, Kathleen L. Donahue, Anne-Katrin Giese, Mark R. Etherton, Ona Wu, Marius de Groot, Wiro Niessen, Meike Vernooij, Natalia S. Rost, Markus D. Schirmer
In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain.
no code implementations • 5 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.
no code implementations • 8 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.
no code implementations • 23 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.
no code implementations • 12 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.
no code implementations • 21 Mar 2018 • Filipe Marques, Florian Dubost, Mariette Kemner-van de Corput, Harm A. W. Tiddens, Marleen de Bruijne
We compare our method with random forest and a single neural network approach.
no code implementations • 16 Feb 2018 • Florian Dubost, Hieab Adams, Gerda Bortsova, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI.
no code implementations • 4 Jun 2017 • Gerda Bortsova, Gijs van Tulder, Florian Dubost, Tingying Peng, Nassir Navab, Aad van der Lugt, Daniel Bos, Marleen de Bruijne
In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge.
no code implementations • 22 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.
no code implementations • 20 Sep 2016 • Florian Dubost, Loic Peter, Christian Rupprecht, Benjamin Gutierrez-Becker, Nassir Navab
We propose a novel hands-free method to interactively segment 3D medical volumes.