Search Results for author: Florian Dubost

Found 31 papers, 9 papers with code

The Hidden Adversarial Vulnerabilities of Medical Federated Learning

no code implementations21 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.

Federated Learning

Exploring adversarial attacks in federated learning for medical imaging

no code implementations10 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.

Federated Learning Privacy Preserving

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

1 code implementation30 Aug 2023 Jianning Li, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Xiaojun Chen, 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, 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, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, 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, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Christos Davatzikos, Behrus Puladi, Jens Kleesiek, Jan Egger

MedShapeNet is created as an alternative to these commonly used shape benchmarks to facilitate the translation of data-driven vision algorithms to medical applications, and it extends the opportunities to adapt SOTA vision algorithms to solve critical medical problems.

Mixed Reality

ATCON: Attention Consistency for Vision Models

1 code implementation18 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.

Event Detection

Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification

1 code implementation10 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.

Classification Decision Making +3

Automated Detection of Patients in Hospital Video Recordings

no code implementations28 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.

EEG Electroencephalogram (EEG) +2

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

Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing

1 code implementation28 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.

BIG-bench Machine Learning Electroencephalogram (EEG) +2

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

2 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

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 Test

Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators

no code implementations17 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.

Gaussian Processes

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

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.

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

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

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