Brennan Nichyporuk

HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling

Daniel Duenias

Brennan Nichyporuk

Tammy Riklin-Raviv

The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Reco… (see more)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.

2024-03-20

ArXiv (preprint)

DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations

Nima Fathi

Amar Kumar

Brennan Nichyporuk

Mohammad Havaei

Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (see more)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.

2024-02-13

MIDL.io/2024/Conference/Submission (unknown)

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

Tal Arbel … (see 47 more)

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

Brennan Nichyporuk

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

B. Calster

Gael Varoquaux

PAUL F. JÄGER

2024-02-12

Nature Methods (published)

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

Florian Buttner

M. Jorge Cardoso

Veronika Cheplygina

JIANXU CHEN … (see 62 more)

Evangelia Christodoulou

BETH A. CIMINI

Keyvan Farahani

LUCIANA FERRER

Gary S. Collins

Adrian Galdran

Bram van Ginneken

Ben Glocker

PATRICK GODAU

Daniel A. Hashimoto

Michael M. Hoffman

Robert Cary Haase

Merel Huisman

Fabian Isensee

Pierre Jannin

CHARLES E. KAHN

DAGMAR KAINMUELLER

BERNHARD KAINZ

ALEXANDROS KARARGYRIS

Jens Kleesiek

Florian Kofler

THIJS KOOI

Annette Kopp-Schneider

Alan Karthikesalingam

H. Kenngott

Michal Kozubek

Anna Kreshuk

Tahsin Kurc

Bennett A. Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

ERIK MEIJERING

Bjoern Menze

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Peter Mattson

Felix Nickel

Jens Petersen

SUSANNE M. RAFELSKI

NASIR RAJPOOT

Mauricio Reyes

MICHAEL A. RIEGLER

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA SHETTY

Ronald M. Summers

Abdel Aziz Taha

ALEKSEI TIULPIN

Sotirios A. Tsaftaris

Ben Van Calster

Gael Varoquaux

ZIV R. YANIV

M. Smeden

PAUL F. JÄGER

Lena Maier-Hein

B. Calster

Manuel Wiesenfarth

Ziv Rafael Yaniv

2024-02-12

Nature methods (published)

Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles

Xing Shen

Hengguan Huang

Brennan Nichyporuk

2023-10-24

ArXiv (preprint)

Debiasing Counterfactuals in the Presence of Spurious Correlations

Amar Kumar

Nima Fathi

Raghav Mehta

Brennan Nichyporuk

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… (see more)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.

2023-10-09

Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging (published)

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Joshua D. Durso-Finley

Jean-Pierre R. Falet

Brennan Nichyporuk

Douglas Arnold

Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (see more)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties,...).

2022-12-04

Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (published)

Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors

Anjun Hu

Jean-Pierre R. Falet

Brennan Nichyporuk

Changjian Shui

Douglas Arnold

Sotirios A. Tsaftaris

We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain l… (see more)esions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies.

2022-11-15

ArXiv (preprint)

Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation

Brennan Nichyporuk

Jillian L. Cardinell

Justin Szeto

Raghav Mehta

Jean-Pierre R. Falet

Douglas Arnold

Sotirios A. Tsaftaris

Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, wh… (see more)ere unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the "ground-truth" label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified.

2022-10-31

ArXiv (preprint)