Publications

FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks

Moses Openja

Paolo Arcaini

Foutse Khomh

Fuyuki Ishikawa

2025-09-10

ACM Transactions on Software Engineering and Methodology (published)

doi.org

arxiv.org

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

Rohan Banerjee

Merve Kaptan

Alexandra Tinnermann

Ali Khatibi

Alice Dabbagh

Christian Büchel

Christian W. Kündig

Csw Law

Dario Pfyffer

David J. Lythgoe

Dimitra Tsivaka

Dimitri Van De Ville

Falk Eippert

Fauziyya Muhammad

Gary H. Glover

Gergely David

Grace Haynes

Jan Haaker

Jonathan C. W. Brooks

Jürgen Finsterbusch … (see 21 more)

Katherine T. Martucci

Kimberly J. Hemmerling

Mahdi Mobarak-Abadi

Mark A. Hoggarth

Matthew A. Howard

Molly G. Bright

Nawal Kinany

Olivia S. Kowalczyk

Patrick Freund

Robert L. Barry

Sean Mackey

Shahabeddin Vahdat

Simon Schading

Stephen B. McMahon

Todd Parish

Véronique Marchand-Pauvert

Yufen Chen

Zachary A. Smith

Kenneth A. Weber

Benjamin De Leener

Julien Cohen-Adad

Abstract Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic functio… (see more)n. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.1 and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared with other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.

2025-09-09

Imaging neuroscience (published)

doi.org

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

Rohan Banerjee

Merve Kaptan

Alexandra Tinnermann

Ali Khatibi

Alice Dabbagh

Christian Büchel

Christian W. Kündig

Christine S. W. Law

Csw Law

Dario Pfyffer

David J. Lythgoe

Dimitra Tsivaka

Dimitri Van De Ville

Falk Eippert

Fauziyya Muhammad

Gary H. Glover

Gergely David

Grace Haynes

Jan Haaker

Jonathan C. W. Brooks … (see 23 more)

Jürgen Finsterbusch

Katherine T. Martucci

Kimberly J. Hemmerling

Mahdi Mobarak-Abadi

Mark A. Hoggarth

Matthew A. Howard

Molly G. Bright

Nawal Kinany

Olivia S. Kowalczyk

Patrick Freund

Robert L. Barry

Sean Mackey

Shahabeddin Vahdat

Simon Schading

Stephen B. McMahon

Todd Parish

Véronique Marchand-Pauvert

Yufen Chen

Zachary A. Smith

KA Weber

Kenneth A. Weber

Benjamin De Leener

Julien Cohen-Adad

Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Prepro… (see more)cessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.0, and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared to other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.

2025-09-09

Imaging neuroscience (published)

doi.org

RL Fine-Tuning Heals OOD Forgetting in SFT

Hangzhan Jin

Sicheng Lyu

Mohammad Hamdaqa

The two-stage fine-tuning paradigm of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has empirically shown better reas… (see more)oning performance than one-stage SFT for the post-training of Large Language Models (LLMs). However, the evolution and mechanism behind the synergy of SFT and RL are still under-explored and inconclusive. In our study, we find the well-known claim"SFT memorizes, RL generalizes"is over-simplified, and discover that: (1) OOD performance peaks at the early stage of SFT and then declines (OOD forgetting), the best SFT checkpoint cannot be captured by training/test loss; (2) the subsequent RL stage does not generate fundamentally better OOD capability, instead it plays an \textbf{OOD restoration} role, recovering the lost reasoning ability during SFT; (3) The recovery ability has boundaries, \ie{} \textbf{if SFT trains for too short or too long, RL cannot recover the lost OOD ability;} (4) To uncover the underlying mechanisms behind the forgetting and restoration process, we employ SVD analysis on parameter matrices, manually edit them, and observe their impacts on model performance. Unlike the common belief that the shift of model capacity mainly results from the changes of singular values, we find that they are actually quite stable throughout fine-tuning. Instead, the OOD behavior strongly correlates with the \textbf{rotation of singular vectors}. Our findings re-identify the roles of SFT and RL in the two-stage fine-tuning and discover the rotation of singular vectors as the key mechanism. %reversing the rotations induced by SFT, which shows recovery from forgetting, whereas imposing the SFT parameter directions onto a RL-tuned model results in performance degradation. Code is available at https://github.com/xiaodanguoguo/RL_Heals_SFT

2025-09-08

ArXiv (preprint)

doi.org

arxiv.org

RL Fine-Tuning Heals OOD Forgetting in SFT

Hangzhan Jin

Sicheng Lyu

Mohammad Hamdaqa

The two-stage fine-tuning paradigm of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has empirically shown better reas… (see more)oning performance than one-stage SFT for the post-training of Large Language Models (LLMs). However, the evolution and mechanism behind the synergy of SFT and RL are still under-explored and inconclusive. In our study, we find the well-known claim"SFT memorizes, RL generalizes"is over-simplified, and discover that: (1) OOD performance peaks at the early stage of SFT and then declines (OOD forgetting), the best SFT checkpoint cannot be captured by training/test loss; (2) the subsequent RL stage does not generate fundamentally better OOD capability, instead it plays an \textbf{OOD restoration} role, recovering the lost reasoning ability during SFT; (3) The recovery ability has boundaries, \ie{} \textbf{if SFT trains for too short or too long, RL cannot recover the lost OOD ability;} (4) To uncover the underlying mechanisms behind the forgetting and restoration process, we employ SVD analysis on parameter matrices, manually edit them, and observe their impacts on model performance. Unlike the common belief that the shift of model capacity mainly results from the changes of singular values, we find that they are actually quite stable throughout fine-tuning. Instead, the OOD behavior strongly correlates with the \textbf{rotation of singular vectors}. Our findings re-identify the roles of SFT and RL in the two-stage fine-tuning and discover the rotation of singular vectors as the key mechanism. %reversing the rotations induced by SFT, which shows recovery from forgetting, whereas imposing the SFT parameter directions onto a RL-tuned model results in performance degradation. Code is available at https://github.com/xiaodanguoguo/RL_Heals_SFT

2025-09-08

ArXiv (preprint)

arxiv.org

An AI system to help scientists write expert-level empirical software

Eser Aygün

Anastasiya Belyaeva

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (see 21 more)

Matthew Abraham

Erica Brand

Anna Bulanova

Jeff Cardille

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

D. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (see more)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-08

ArXiv (preprint)

doi.org

arxiv.org

An AI system to help scientists write expert-level empirical software

Eser Aygün

Anastasiya Belyaeva

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (see 21 more)

Matthew Abraham

Erica Brand

Anna Bulanova

Jeff Cardille

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

Daniel J. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (see more)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-08

ArXiv (preprint)

doi.org

arxiv.org

An AI system to help scientists write expert-level empirical software

Eser Aygün

Anastasiya Belyaeva

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (see 21 more)

Matthew Abraham

Erica Brand

Anna Bulanova

Jeff Cardille

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

D. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (see more)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-08

ArXiv (preprint)

arxiv.org

An AI system to help scientists write expert-level empirical software

Eser Aygün

Anastasiya Belyaeva

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (see 21 more)

Matthew Abraham

Erica Brand

Anna Bulanova

Jeff Cardille

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

D. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (see more)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-08

ArXiv (preprint)

arxiv.org

Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance

Mohamed Mohamed

Brennan Nichyporuk

Douglas Arnold

Tal Arbel

Vision-language models have demonstrated impressive capabilities in generating 2D images under various conditions; however, the success of t… (see more)hese models is largely enabled by extensive, readily available pretrained foundation models. Critically, comparable pretrained models do not exist for 3D, significantly limiting progress. As a result, the potential of vision-language models to produce high-resolution 3D counterfactual medical images conditioned solely on natural language remains unexplored. Addressing this gap would enable powerful clinical and research applications, such as personalized counterfactual explanations, simulation of disease progression, and enhanced medical training by visualizing hypothetical conditions in realistic detail. Our work takes a step toward this challenge by introducing a framework capable of generating high-resolution 3D counterfactual medical images of synthesized patients guided by free-form language prompts. We adapt state-of-the-art 3D diffusion models with enhancements from Simple Diffusion and incorporate augmented conditioning to improve text alignment and image quality. To our knowledge, this is the first demonstration of a language-guided native-3D diffusion model applied to neurological imaging, where faithful three-dimensional modeling is essential. On two neurological MRI datasets, our framework simulates varying counterfactual lesion loads in Multiple Sclerosis and cognitive states in Alzheimer's disease, generating high-quality images while preserving subject fidelity. Our results lay the groundwork for prompt-driven disease progression analysis in 3D medical imaging. Project link - https://lesupermomo.github.io/imagining-alternatives/.

2025-09-07

ArXiv (preprint)

arxiv.org

Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance

Mohamed Mohamed

Brennan Nichyporuk

Douglas Arnold

Tal Arbel

Vision-language models have demonstrated impressive capabilities in generating 2D images under various conditions; however, the success of t… (see more)hese models is largely enabled by extensive, readily available pretrained foundation models. Critically, comparable pretrained models do not exist for 3D, significantly limiting progress. As a result, the potential of vision-language models to produce high-resolution 3D counterfactual medical images conditioned solely on natural language remains unexplored. Addressing this gap would enable powerful clinical and research applications, such as personalized counterfactual explanations, simulation of disease progression, and enhanced medical training by visualizing hypothetical conditions in realistic detail. Our work takes a step toward this challenge by introducing a framework capable of generating high-resolution 3D counterfactual medical images of synthesized patients guided by free-form language prompts. We adapt state-of-the-art 3D diffusion models with enhancements from Simple Diffusion and incorporate augmented conditioning to improve text alignment and image quality. To our knowledge, this is the first demonstration of a language-guided native-3D diffusion model applied to neurological imaging, where faithful three-dimensional modeling is essential. On two neurological MRI datasets, our framework simulates varying counterfactual lesion loads in Multiple Sclerosis and cognitive states in Alzheimer's disease, generating high-quality images while preserving subject fidelity. Our results lay the groundwork for prompt-driven disease progression analysis in 3D medical imaging. Project link - https://lesupermomo.github.io/imagining-alternatives/.

2025-09-07

ArXiv (preprint)

arxiv.org