215 citations · 352 across the 8 of their papers we have counts for
9 papers
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…
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…
Radiomic Feature Stability Analysis based on Probabilistic Segmentations
Christoph Haarburger, Justus Schock, Daniel Truhn +4
Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled…
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Christoph Haarburger, Michael Baumgartner, Daniel Truhn +5
Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified…
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Christoph Haarburger, Philippe Weitz, Oliver Rippel +1
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image d…