232 citations · 573 across the 29 of their papers we have counts for
31 papers
Improving Calibration of Black-Box Radiology AI Using Test-Time Augmentation
Nathan Le, Magdalini Paschali, Arogya Koirala +5
Radiology AI systems increasingly inform clinical decisions such as triage, follow-up imaging, and treatment planning. For these decisions to be made safely, model outputs must be…
Gradient-Stable Attention Heads Signal LLM Correctness
Sophie Ostmeier, Brian Axelrod, Maya Varma +6
Large language models (LLMs) often generate plausible yet incorrect answers, posing risks in safety-critical settings such as medicine. Human evaluation is expensive, and LLM-as-ju…
Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data
Stefania L. Moroianu, Christian Bluethgen, Pierre Chambon +8
Achieving robust performance and fairness across diverse patient populations remains a challenge in developing clinically deployable deep learning models for diagnostic imaging. Sy…
MedVAL: Toward Expert-Level Medical Text Validation with Language Models
Asad Aali, Vasiliki Bikia, Maya Varma +24
With the growing use of language models (LMs) in clinical environments, there is an immediate need to evaluate the accuracy and safety of LM-generated medical text. Currently, such…
Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)
Zhongnan Fang, Andrew Johnston, Lina Cheuy +10
Artificial intelligence (AI) tools for radiology are commonly unmonitored once deployed. The lack of real-time case-by-case assessments of AI prediction confidence requires users t…
Foundation Models in Radiology: What, How, When, Why and Why Not
Magdalini Paschali, Zhihong Chen, Louis Blankemeier +6
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Su…
SOE: SO(3)-Equivariant 3D MRI Encoding
Shizhe He, Magdalini Paschali, Jiahong Ouyang +3
Representation learning has become increasingly important, especially as powerful models have shifted towards learning latent representations before fine-tuning for downstream task…
Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging
Magdalini Paschali, Yu Hang Jiang, Spencer Siegel +4
Recent advancements in medicine have confirmed that brain disorders often comprise multiple subtypes of mechanisms, developmental trajectories, or severity levels. Such heterogenei…
Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset
Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37
The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
Zhihong Chen, Maya Varma, Justin Xu +20
Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant…
Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET
Francesca De Benetti, Walter Simson, Magdalini Paschali +5
Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes. Voxel-wise physiolog…
Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning
Walter A. Simson, Magdalini Paschali, Vasiliki Sideri-Lampretsa +2
Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound spe…
Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing
Magdalini Paschali, Qingyu Zhao, Ehsan Adeli +1
A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life even…
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
Tobias Czempiel, Coco Rogers, Matthias Keicher +7
Quantifying COVID-19 infection over time is an important task to manage the hospitalization of patients during a global pandemic. Recently, deep learning-based approaches have been…
Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study
Tobias Czempiel, Aidean Sharghi, Magdalini Paschali +2
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis. So far many different…
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Michelle Xiao-Lin Foo, Seong Tae Kim, Magdalini Paschali +6
Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic…
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni +7
During the first wave of COVID-19, hospitals were overwhelmed with the high number of admitted patients. An accurate prediction of the most likely individual disease progression ca…
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis
Christoph Berger, Magdalini Paschali, Ben Glocker +1
Image classification models deployed in the real world may receive inputs outside the intended data distribution. For critical applications such as clinical decision making, it is…
Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
Maria Tirindelli, Christine Eilers, Walter Simson +3
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the t…
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
Tobias Czempiel, Magdalini Paschali, Daniel Ostler +3
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the…
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Seong Tae Kim, Leili Goli, Magdalini Paschali +7
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity a…
Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning
Hannes Hase, Mohammad Farid Azampour, Maria Tirindelli +4
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-t…
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Tobias Czempiel, Magdalini Paschali, Matthias Keicher +4
Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support…
Radar Emitter Classification with Attribute-specific Recurrent Neural Networks
Paolo Notaro, Magdalini Paschali, Carsten Hopke +2
Radar pulse streams exhibit increasingly complex temporal patterns and can no longer rely on a purely value-based analysis of the pulse attributes for the purpose of emitter classi…