11 citations · 24 across the 22 of their papers we have counts for
26 papers
The Value of System 1 Models in Medicine
Xiufeng Yang, Konstantinos Vrettos, Zeineb Ben Chaaben +17
Many clinical decisions are a choice among a few fixed options, taken quickly and in large numbers. Such decisions also need an uncertainty estimate to know when to defer to a clin…
Autonomous Structuring of Radiology Reports Across Modalities at Archive Scale Using an Open-Weight Large Language Model
Friedrich Puttkammer, Fabian Drexel, Marlene Fritzsche +19
Purpose: To develop and evaluate an open-weight large language model (LLM) pipeline that converts an entire archive of free-text radiology reports into structured reports without h…
RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding
Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu +15
Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, s…
Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging
Bernhard Kainz, Johanna P Mueller, Matthew Baugh +1
Zero-shot anomaly localisation via vision-language models (VLMs) offers a compelling approach for rare pathology detection, yet its performance is fundamentally limited by the abse…
Measuring and Aligning Abstraction in Vision-Language Models with Medical Taxonomies
Ben Schaper, Maxime Di Folco, Bernhard Kainz +2
Vision-Language Models show strong zero-shot performance for chest X-ray classification, but standard flat metrics fail to distinguish between clinically minor and severe errors. T…
LocBAM: Advancing 3D Patch-Based Image Segmentation by Integrating Location Contex
Donnate Hooft, Stefan M. Fischer, Cosmin Bercea +2
Patch-based methods are widely used in 3D medical image segmentation to address memory constraints in processing high-resolution volumetric data. However, these approaches often ne…
TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks
Johannes Kiechle, Stefan M. Fischer, Daniel M. Lang +5
The sharp rise in medical tomography examinations has created a demand for automated systems that can reliably extract informative features for downstream tasks such as tumor chara…
Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound
Chun Kit Wong, Anders N. Christensen, Cosmin I. Bercea +3
Reliable out-of-distribution (OOD) detection is important for safe deployment of deep learning models in fetal ultrasound amidst heterogeneous image characteristics and clinical se…
Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding
Jun Li, Che Liu, Wenjia Bai +4
In this work, we address the problem of grounding abnormalities in medical images, where the goal is to localize clinical findings based on textual descriptions. While generalist V…
Semantic Alignment of Unimodal Medical Text and Vision Representations
Maxime Di Folco, Emily Chan, Marta Hasny +2
General-purpose AI models, particularly those designed for text and vision, demonstrate impressive versatility across a wide range of deep-learning tasks. However, they often under…
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions
Jun Li, Che Liu, Wenjia Bai +3
Visual Language Models (VLMs) have demonstrated impressive capabilities in visual grounding tasks. However, their effectiveness in the medical domain, particularly for abnormality…
Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations
Sameer Ambekar, Julia A. Schnabel, Cosmin I. Bercea
Deep learning models in medical imaging often encounter challenges when adapting to new clinical settings unseen during training. Test-time adaptation offers a promising approach t…
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks
Peiran Wu, Che Liu, Canyu Chen +3
Advancements in Multimodal Large Language Models (MLLMs) have significantly improved medical task performance, such as Visual Question Answering (VQA) and Report Generation (RG). H…
Unsupervised Analysis of Alzheimer's Disease Signatures using 3D Deformable Autoencoders
Mehmet Yigit Avci, Emily Chan, Veronika Zimmer +4
With the increasing incidence of neurodegenerative diseases such as Alzheimer's Disease (AD), there is a need for further research that enhances detection and monitoring of the dis…
Interpretable Representation Learning of Cardiac MRI via Attribute Regularization
Maxime Di Folco, Cosmin I. Bercea, Emily Chan +1
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. Several approaches have been recently considered…
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability
Jun Li, Su Hwan Kim, Philip Müller +5
This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the i…
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert +1
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents. Nonetheless, standard approach…
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert +1
The increasing complexity of medical imaging data underscores the need for advanced anomaly detection methods to automatically identify diverse pathologies. Current methods face ch…
Attribute Regularized Soft Introspective Variational Autoencoder for Interpretable Cardiac Disease Classification
Maxime Di Folco, Cosmin I. Bercea, Julia A. Schnabel
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. In this paper, we propose a novel interpretable…
Bias in Unsupervised Anomaly Detection in Brain MRI
Cosmin I. Bercea, Esther Puyol-Antón, Benedikt Wiestler +3
Unsupervised anomaly detection methods offer a promising and flexible alternative to supervised approaches, holding the potential to revolutionize medical scan analysis and enhance…
Attribute Regularized Soft Introspective VAE: Towards Cardiac Attribute Regularization Through MRI Domains
Maxime Di Folco, Cosmin Bercea, Julia A. Schnabel
Deep generative models have emerged as influential instruments for data generation and manipulation. Enhancing the controllability of these models by selectively modifying data att…
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models
Cosmin I. Bercea, Michael Neumayr, Daniel Rueckert +1
The introduction of diffusion models in anomaly detection has paved the way for more effective and accurate image reconstruction in pathologies. However, the current limitations in…
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection
Cosmin I Bercea, Benedikt Wiestler, Daniel Rueckert +1
Early and accurate disease detection is crucial for patient management and successful treatment outcomes. However, the automatic identification of anomalies in medical images can b…
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI
Daniel M. Lang, Eli Schwartz, Cosmin I. Bercea +2
Self-supervised models allow (pre-)training on unlabeled data and therefore have the potential to overcome the need for large annotated cohorts. One leading self-supervised model i…