Publications

PRISM: High-Resolution&Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar
Anita Kriz
Mohammad Havaei
Tal Arbel
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (see more)ta imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.
2025-03-27
MIDL.io/2025/Conference (oral)
doi.org
openreview.net
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Bechard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
Chris Pal
David Vazquez
Spandana Gella
Sai Rajeswar
Perouz Taslakian
2025-03-27
ArXiv (preprint)
doi.org
arxiv.org
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Bechard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
Chris Pal
David Vazquez
Spandana Gella
Sai Rajeswar
Perouz Taslakian
2025-03-27
ArXiv (preprint)
arxiv.org
arxiv.org
A Text-guided Protein Design Framework
Shengchao Liu
Yutao Zhu
Yanjing Li
Zhuoxinran Li
Jiarui Lu
Zhao Xu
Weili Nie
Anthony Gitter
Chaowei Xiao
Jian Tang
Arvind Ramanathan
Hongyu Guo
Animashree Anandkumar
2025-03-27
Nature Machine Intelligence (published)
doi.org
arxiv.org
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Arthur Ouaknine
Etienne Lalibert'e
Yoshua Bengio
David Rolnick
Hugo Larochelle
2025-03-26
ArXiv (preprint)
arxiv.org
arxiv.org
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Arthur Ouaknine
Etienne Lalibert'e
Yoshua Bengio
David Rolnick
Hugo Larochelle
2025-03-26
ArXiv (preprint)
doi.org
arxiv.org
A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer.
Björn Morén
Hossein Jafarzadeh
Shirin A. Enger
2025-03-26
Comput. Biol. Medicine (published)
doi.org
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
Masoumeh Sharafi
Emma Ollivier
Muhammad Osama Zeeshan
Soufiane Belharbi
Marco Pedersoli
Alessandro Lameiras Koerich
Simon Bacon
Eric Granger
2025-03-26
ArXiv (preprint)
arxiv.org
arxiv.org
Perspectives on optimizing transport systems with supply-dependent demand
Emma Frejinger
Mike Hewitt
2025-03-26
INFOR. Information systems and operational research (published)
doi.org
Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems
Dmytro Humeniuk
Foutse Khomh
2025-03-26
ArXiv (preprint)
arxiv.org
arxiv.org
Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems
Dmytro Humeniuk
Foutse Khomh
2025-03-26
ArXiv (preprint)
doi.org
arxiv.org
Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images.
Pedro Vianna
Paria Mehrbod
Muawiz Chaudhary
Michael Eickenberg
Guy Wolf
Eugene Belilovsky
An Tang
Guy Cloutier
Ultrasound is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its non-invasiveness and… (see more) availability. Deep learning methods have attracted considerable interest in this field, as they are capable of learning patterns in a collection of images and achieve clinically comparable levels of accuracy in steatosis grading. However, variations in patient populations, acquisition protocols, equipment, and operator expertise across clinical sites can introduce domain shifts that reduce model performance when applied outside the original training setting. In response, unsupervised domain adaptation techniques are being investigated to address these shifts, allowing models to generalize more effectively across diverse clinical environments. In this work, we propose a test-time batch normalization technique designed to handle domain shift, especially for changes in label distribution, by adapting selected features of batch normalization layers in a trained convolutional neural network model. This approach operates in an unsupervised manner, allowing robust adaptation to new distributions without access to label data. The method was evaluated on two abdominal ultrasound datasets collected at different institutions, assessing its capability in mitigating domain shift for hepatic steatosis classification. The proposed method reduced the mean absolute error in steatosis grading by 37% and improved the area under the receiver operating characteristic curve for steatosis detection from 0.78 to 0.97, compared to non-adapted models. These findings demonstrate the potential of the proposed method to address domain shift in ultrasound-based hepatic steatosis diagnosis, minimizing risks associated with deploying trained models in various clinical settings.
2025-03-26
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (published)
doi.org