2 citations · 8 across the 15 of their papers we have counts for
19 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…
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…
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…
Boosting Radiology Report Generation by Infusing Comparison Prior
Sanghwan Kim, Farhad Nooralahzadeh, Morteza Rohanian +5
Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains: these models often la…
Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III
Aron N. Horvath, Matteo Berchier, Farhad Nooralahzadeh +2
Background: Federated learning methods offer the possibility of training machine learning models on privacy-sensitive data sets, which cannot be easily shared. Multiple regulations…
Improving the Cross-Lingual Generalisation in Visual Question Answering
Farhad Nooralahzadeh, Rico Sennrich
While several benefits were realized for multilingual vision-language pretrained models, recent benchmarks across various tasks and languages showed poor cross-lingual generalisati…
Progressive Transformer-Based Generation of Radiology Reports
Farhad Nooralahzadeh, Nicolas Perez Gonzalez, Thomas Frauenfelder +2
Inspired by Curriculum Learning, we propose a consecutive (i.e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two ste…
Low-Resource Adaptation of Neural NLP Models
Farhad Nooralahzadeh
Real-world applications of natural language processing (NLP) are challenging. NLP models rely heavily on supervised machine learning and require large amounts of annotated data. Th…
Zero-Shot Cross-Lingual Transfer with Meta Learning
Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva +1
Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance. This is…
Syntactic Dependency Representations in Neural Relation Classification
Farhad Nooralahzadeh, Lilja Øvrelid
We investigate the use of different syntactic dependency representations in a neural relation classification task and compare the CoNLL, Stanford Basic and Universal Dependencies s…
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers
Farhad Nooralahzadeh, Lilja Øvrelid, Jan Tore Lønning
This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest d…