19 citations · 25 across the 9 of their papers we have counts for
9 papers
On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction
Tianyu Han, Sven Nebelung, Firas Khader +2
Denoising diffusion models offer a promising approach to accelerating magnetic resonance imaging (MRI) and producing diagnostic-level images in an unsupervised manner. However, our…
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization
Firas Khader, Omar S. M. El Nahhas, Tianyu Han +4
The Transformer model has been pivotal in advancing fields such as natural language processing, speech recognition, and computer vision. However, a critical limitation of this mode…
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
Omar S. M. El Nahhas, Georg Wölflein, Marta Ligero +5
Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting. Recently, the prediction of continuous biomarkers through regress…
An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs
Patrick Wienholt, Alexander Hermans, Firas Khader +5
This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regres…
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining
Tianyu Han, Laura Žigutytė, Luisa Huck +9
Detecting misleading patterns in automated diagnostic assistance systems, such as those powered by Artificial Intelligence, is critical to ensuring their reliability, particularly…
Medical Foundation Models are Susceptible to Targeted Misinformation Attacks
Tianyu Han, Sven Nebelung, Firas Khader +9
Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains, holding promising potential for diverse medical applications…