6 citations · 13 across the 16 of their papers we have counts for
18 papers
Sparse Concept Channels in Frozen 3D CT Vision Encoders
Farhad Nooralahzadeh, Lea Bogensperger, Christian Bluethgen +1
Large vision-language models are becoming increasingly dominant in 3D medical image interpretation, but we rarely know <i>which</i> internal units encode clinical findings or <i>wh…
RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
Mélanie Roschewitz, Kenneth Styppa, Yitian Tao +10
Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods large…
Universal Boosts, Specific Suppressors: Sparse Autoencoder Steering of Medical Vision-Language Models
Farhad Nooralahzadeh, Benjamin Gundersen, Nicolas Deperrois +7
Medical vision-language models (VLMs) often hallucinate findings when generating chest X-ray reports: they fabricate findings that are not present in the image, miss important ones…
Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning
Benjamin Gundersen, Nicolas Deperrois, Samuel Ruiperez-Campillo +5
Recent advances in vision-language models (VLMs) have improved Chest X-ray (CXR) interpretation in multiple aspects. However, many medical VLMs rely solely on supervised fine-tunin…
Structure is Supervision: Multiview Masked Autoencoders for Radiology
Sonia Laguna, Andrea Agostini, Alain Ryser +9
Building robust medical machine learning systems requires pretraining strategies that exploit the intrinsic structure present in clinical data. We introduce Multiview Masked Autoen…
Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges
Christian Bluethgen, Dave Van Veen, Daniel Truhn +8
Building agents, systems that perceive and act upon their environment with a degree of autonomy, has long been a focus of AI research. This pursuit has recently become vastly more…
Optimizing Speech Language Models for Acoustic Consistency
Morteza Rohanian, Michael Krauthammer
We study speech language models that incorporate semantic initialization and planning losses to achieve robust and consistent generation. Our approach initializes speech tokens wit…
Leveraging the Structure of Medical Data for Improved Representation Learning
Andrea Agostini, Sonia Laguna, Alain Ryser +7
Building generalizable medical AI systems requires pretraining strategies that are data-efficient and domain-aware. Unlike internet-scale corpora, clinical datasets such as MIMIC-C…
Towards Scalable and Cross-Lingual Specialist Language Models for Oncology
Morteza Rohanian, Tarun Mehra, Nicola Miglino +3
Clinical oncology generates vast, unstructured data that often contain inconsistencies, missing information, and ambiguities, making it difficult to extract reliable insights for d…
Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrum
Morteza Rohanian, Roya M. Hüppi, Farhad Nooralahzadeh +8
Capturing subtle speech disruptions across the psychosis spectrum is challenging because of the inherent variability in speech patterns. This variability reflects individual differ…
RadVLM: A Multitask Conversational Vision-Language Model for Radiology
Nicolas Deperrois, Hidetoshi Matsuo, Samuel Ruipérez-Campillo +12
The widespread use of chest X-rays (CXRs), coupled with a shortage of radiologists, has driven growing interest in automated CXR analysis and AI-assisted reporting. While existing…
Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification
Michael Vollenweider, Manuel Schürch, Chiara Rohrer +3
Precision medicine has the potential to tailor treatment decisions to individual patients using machine learning (ML) and artificial intelligence (AI), but it faces significant cha…
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
Heath Arthur-Loui, Amina Mollaysa, Michael Krauthammer
De novo molecule design has become a highly active research area, advanced significantly through the use of state-of-the-art generative models. Despite these advances, several fund…
Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis
Cécile Trottet, Manuel Schürch, Ahmed Allam +11
We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Scle…
Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data
Manuel Schürch, Laura Boos, Viola Heinzelmann-Schwarz +4
AI-driven precision oncology has the transformative potential to reshape cancer treatment by leveraging the power of AI models to analyze the interaction between complex patient ch…
Radiology-Aware Model-Based Evaluation Metric for Report Generation
Amos Calamida, Farhad Nooralahzadeh, Morteza Rohanian +3
We propose a new automated evaluation metric for machine-generated radiology reports using the successful COMET architecture adapted for the radiology domain. We train and publish…
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
Cécile Trottet, Manuel Schürch, Ahmed Allam +6
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to fin…
Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series
Xingyu Chen, Xiaochen Zheng, Amina Mollaysa +3
Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampl…