215 citations · 571 across the 18 of their papers we have counts for
18 papers
Auditable CT Phenotyping Through Report-derived Radiological Observations
Riga Wu, Walter Witschey, Yicheng Li +9
Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read disease-specific findings or sho…
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
Gustav Müller-Franzes, Firas Khader, Robert Siepmann +4
MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. Wh…
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
Felix J. Dorfner, Amin Dada, Felix Busch +8
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this appro…
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…
LongHealth: A Question Answering Benchmark with Long Clinical Documents
Lisa Adams, Felix Busch, Tianyu Han +7
Background: Recent advancements in large language models (LLMs) offer potential benefits in healthcare, particularly in processing extensive patient records. However, existing benc…
From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties
Felix Busch, Tianyu Han, Marcus Makowski +3
The study evaluates and compares GPT-4 and GPT-4Vision for radiological tasks, suggesting GPT-4Vision may recognize radiological features from images, thereby enhancing its diagnos…
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…
Large Language Models Streamline Automated Machine Learning for Clinical Studies
Soroosh Tayebi Arasteh, Tianyu Han, Mahshad Lotfinia +4
A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical da…
Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs
Firas Khader, Gustav Müller-Franzes, Tianyu Han +4
Computed Tomography (CT) scans provide detailed and accurate information of internal structures in the body. They are constructed by sending x-rays through the body from different…
Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers
Firas Khader, Jakob Nikolas Kather, Tianyu Han +4
Whole-Slide Imaging allows for the capturing and digitization of high-resolution images of histological specimen. An automated analysis of such images using deep learning models is…
MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou +6
As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fi…
Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis
Firas Khader, Gustav Mueller-Franzes, Tianci Wang +9
Multimodal deep learning has been used to predict clinical endpoints and diagnoses from clinical routine data. However, these models suffer from scaling issues: they have to learn…
Diffusion Probabilistic Models beat GANs on Medical Images
Gustav Müller-Franzes, Jan Moritz Niehues, Firas Khader +8
The success of Deep Learning applications critically depends on the quality and scale of the underlying training data. Generative adversarial networks (GANs) can generate arbitrary…
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh +12
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input…
Image prediction of disease progression by style-based manifold extrapolation
Tianyu Han, Jakob Nikolas Kather, Federico Pedersoli +11
Disease-modifying management aims to prevent deterioration and progression of the disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is often…
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Tianyu Han, Sven Nebelung, Federico Pedersoli +8
Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversaria…