31 papers
Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology
Franciskus Xaverius Erick, Johanna Paula Müller, Bernhard Kainz
Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology. However, fine-tuning billion…
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…
Vision-Language Models as Zero-Annotation Oracles in Histopathology
Vishal Jain, Giorgio Buzzanca, Sarah Cechnicka +6
Foreground segmentation is the critical first step of every computational pathology pipeline, yet existing methods rely on hand-tuned heuristics or supervised models that overfit t…
Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
Luca Hagen, Johanna P. Müller, Weitong Zhang +2
Small vision-language models (2-8B) are well-suited for clinical deployment due to privacy constraints, limited connectivity, and low-latency requirements favouring on-device or on…
Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology
Zhe Li, Bernhard Kainz
Deep learning has become prevalent in computational pathology pipelines that support tasks such as cancer screening and digital pathology analysis. However, the susceptibility of n…
The Learnability Gap in Medical Latent Diffusion
Mischa Dombrowski, Felix Nützel, Bernhard Kainz
Generative data augmentation with latent diffusion models is a promising strategy for addressing class imbalance in medical imaging, yet current approaches focus on perceptual fide…