232 citations · 573 across the 29 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
Magdalini Paschali, Walter Simson, Abhijit Guha Roy +4
In this paper we propose a novel augmentation technique that improves not only the performance of deep neural networks on clean test data, but also significantly increases their ro…