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Brennan Nichyporuk

Scientifique de recherche, Innovation, développement et technologies

Publications

Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
Gian Mario Favero
Ge Ya Luo
Douglas Arnold
Image-based personalized medicine has the potential to transform healthcare, particularly for diseases that exhibit heterogeneous progressio… (voir plus)n such as Multiple Sclerosis (MS). In this work, we introduce the first treatment-aware spatio-temporal diffusion model that is able to generate future masks demonstrating lesion evolution in MS. Our voxel-space approach incorporates multi-modal patient data, including MRI and treatment information, to forecast new and enlarging T2 (NET2) lesion masks at a future time point. Extensive experiments on a multi-centre dataset of 2131 patient 3D MRIs from randomized clinical trials for relapsing-remitting MS demonstrate that our generative model is able to accurately predict NET2 lesion masks for patients across six different treatments. Moreover, we demonstrate our model has the potential for real-world clinical applications through downstream tasks such as future lesion count and location estimation, binary lesion activity classification, and generating counterfactual future NET2 masks for several treatments with different efficacies. This work highlights the potential of causal, image-based generative models as powerful tools for advancing data-driven prognostics in MS.
Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
Gian Mario Favero
Ge Ya Luo
Douglas Arnold
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Discriminative classifiers have become a foundational tool in deep learning for medical imaging, excelling at learning separable features of… (voir plus) complex data distributions. However, these models often need careful design, augmentation, and training techniques to ensure safe and reliable deployment. Recently, diffusion models have become synonymous with generative modeling in 2D. These models showcase robustness across a range of tasks including natural image classification, where classification is performed by comparing reconstruction errors across images generated for each possible conditioning input. This work presents the first exploration of the potential of class conditional diffusion models for 2D medical image classification. First, we develop a novel majority voting scheme shown to improve the performance of medical diffusion classifiers. Next, extensive experiments on the CheXpert and ISIC Melanoma skin cancer datasets demonstrate that foundation and trained-from-scratch diffusion models achieve competitive performance against SOTA discriminative classifiers without the need for explicit supervision. In addition, we show that diffusion classifiers are intrinsically explainable, and can be used to quantify the uncertainty of their predictions, increasing their trustworthiness and reliability in safety-critical, clinical contexts. Further information is available on our project page: https://faverogian.github.io/med-diffusion-classifier.github.io/
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
Exploring Compound Loss Functions for Brain Tumor Segmentation
HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
Daniel Duenias
Tammy Riklin-Raviv
The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Reco… (voir plus)rds (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and the generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multiclass Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods. The code, enclosed to this manuscript will be made publicly available.
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (voir plus)with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.
Metrics reloaded: Pitfalls and recommendations for image analysis validation
Lena Maier-Hein
Annika Reinke
Evangelia Christodoulou
Ben Glocker
PATRICK GODAU
Fabian Isensee
Jens Kleesiek
Michal Kozubek
Mauricio Reyes
MICHAEL A. RIEGLER
Manuel Wiesenfarth
Michael Baumgartner
Matthias Eisenmann
DOREEN HECKMANN-NÖTZEL
A. EMRE KAVUR
TIM RÄDSCH
Minu Dietlinde Tizabi
LAURA ACION
Michela Antonelli
Spyridon Bakas
Peter Bankhead
Allison Benis
M. Jorge Cardoso
Veronika Cheplygina
BETH A. CIMINI
Gary S. Collins
Keyvan Farahani
Bram van Ginneken
Daniel A. Hashimoto
Michael M. Hoffman
Merel Huisman
Pierre Jannin
CHARLES E. KAHN
Alexandros Karargyris
Alan Karthikesalingam
H. Kenngott
Annette Kopp-Schneider
Anna Kreshuk
Tahsin Kurc
Bennett Landman
GEERT LITJENS
Amin Madani
Klaus Maier-Hein
Anne L. Martel
Peter Mattson
ERIK MEIJERING
Bjoern Menze
David Moher
KAREL G.M. MOONS
Henning Müller
Felix Nickel
Jens Petersen
NASIR RAJPOOT
Nicola Rieke
Julio Saez-Rodriguez
Clarisa S'anchez Guti'errez
SHRAVYA SHETTY
M. Smeden
Carole H. Sudre
Ronald M. Summers
Abdel Aziz Taha
Sotirios A. Tsaftaris
Ben Van Calster
Gael Varoquaux
PAUL F. JÄGER
Understanding metric-related pitfalls in image analysis validation
Annika Reinke
Minu Dietlinde Tizabi
Michael Baumgartner
Matthias Eisenmann
DOREEN HECKMANN-NÖTZEL
A. EMRE KAVUR
TIM RÄDSCH
Carole H. Sudre
LAURA ACION
Michela Antonelli
Spyridon Bakas
Allison Benis
Arriel Benis
Matthew Blaschko
FLORIAN BUETTNER
M. Jorge Cardoso
Veronika Cheplygina
JIANXU CHEN
Evangelia Christodoulou … (voir 59 de plus)
BETH A. CIMINI
Gary S. Collins
Keyvan Farahani
LUCIANA FERRER
Adrian Galdran
Bram van Ginneken
Ben Glocker
PATRICK GODAU
Robert Cary Haase
Daniel A. Hashimoto
Michael M. Hoffman
Merel Huisman
Fabian Isensee
Pierre Jannin
CHARLES E. KAHN
DAGMAR KAINMUELLER
BERNHARD KAINZ
Alexandros Karargyris
Alan Karthikesalingam
H. Kenngott
Jens Kleesiek
Florian Kofler
THIJS KOOI
Annette Kopp-Schneider
Michal Kozubek
Anna Kreshuk
Tahsin Kurc
BENNETT A. LANDMAN
GEERT LITJENS
Amin Madani
Klaus Maier-Hein
Anne L. Martel
Peter Mattson
ERIK MEIJERING
Bjoern Menze
KAREL G.M. MOONS
Henning Müller
Felix Nickel
Jens Petersen
SUSANNE M. RAFELSKI
NASIR RAJPOOT
Mauricio Reyes
MICHAEL A. RIEGLER
Nicola Rieke
Julio Saez-Rodriguez
Clara I. Sánchez
SHRAVYA SHETTY
M. Smeden
Ronald M. Summers
Abdel Aziz Taha
ALEKSEI TIULPIN
Sotirios A. Tsaftaris
Ben Van Calster
Gael Varoquaux
Manuel Wiesenfarth
ZIV R. YANIV
PAUL F. JÄGER
Lena Maier-Hein
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (voir plus)with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.
Debiasing Counterfactuals in the Presence of Spurious Correlations
Raghav Mehta
Jean-Pierre R. Falet
Sotirios A. Tsaftaris
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correl… (voir plus)ations (i.e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest. This would thereby limit their ability to generalize across the population. Explainability based on counterfactual image generation can be used to expose the confounders but does not provide a strategy to mitigate the bias. In this work, we introduce the first end-to-end training framework that integrates both (i) popular debiasing classifiers (e.g. distributionally robust optimization (DRO)) to avoid latching onto the spurious correlations and (ii) counterfactual image generation to unveil generalizable imaging markers of relevance to the task. Additionally, we propose a novel metric, Spurious Correlation Latching Score (SCLS), to quantify the extent of the classifier reliance on the spurious correlation as exposed by the counterfactual images. Through comprehensive experiments on two public datasets (with the simulated and real visual artifacts), we demonstrate that the debiasing method: (i) learns generalizable markers across the population, and (ii) successfully ignores spurious correlations and focuses on the underlying disease pathology.