25 citations · 107 across the 32 of their papers we have counts for
16 papers · 1 filter
Abnormality-Driven Representation Learning for Radiology Imaging
Marta Ligero, Tim Lenz, Georg Wölflein +3
To date, the most common approach for radiology deep learning pipelines is the use of end-to-end 3D networks based on models pre-trained on other tasks, followed by fine-tuning on…
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…
Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning
Tim Lenz, Peter Neidlinger, Marta Ligero +3
Representation learning of pathology whole-slide images (WSIs) has primarily relied on weak supervision with Multiple Instance Learning (MIL). This approach leads to slide represen…
Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer
Gesa Mittmann, Sara Laiouar-Pedari, Hendrik A. Mehrtens +63
The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artifi…
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
Tirtha Chanda, Sarah Haggenmueller, Tabea-Clara Bucher +19
Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' c…
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
Jacqueline Lammert, Nicole Pfarr, Leonid Kuligin +16
Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poo…
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology
Peter Neidlinger, Omar S. M. El Nahhas, Hannah Sophie Muti +13
Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information. However, there is…
Prompt Injection Attacks on Large Language Models in Oncology
Jan Clusmann, Dyke Ferber, Isabella C. Wiest +5
Vision-language artificial intelligence models (VLMs) possess medical knowledge and can be employed in healthcare in numerous ways, including as image interpreters, virtual scribes…
End-To-End Clinical Trial Matching with Large Language Models
Dyke Ferber, Lars Hilgers, Isabella C. Wiest +13
Matching cancer patients to clinical trials is essential for advancing treatment and patient care. However, the inconsistent format of medical free text documents and complex trial…
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…
Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology
Dyke Ferber, Omar S. M. El Nahhas, Georg Wölflein +11
Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of…
In-context learning enables multimodal large language models to classify cancer pathology images
Dyke Ferber, Georg Wölflein, Isabella C. Wiest +8
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this proce…
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology
Tim Lenz, Omar S. M. El Nahhas, Marta Ligero +1
Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations…
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…
Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data
Salman Ul Hassan Dar, Marvin Seyfarth, Isabelle Ayx +11
AI models present a wide range of applications in the field of medicine. However, achieving optimal performance requires access to extensive healthcare data, which is often not rea…