5 papers
No Data? No Problem: Robust Vision-Tabular Learning with Missing Values
Marta Hasny, Laura Daza, Keno Bressem +2
Large-scale medical biobanks provide imaging data complemented by extensive tabular information, such as clinical measurements or demographics. However, this abundance of tabular a…
Tables Guide Vision: Learning to See the Heart through Tabular Data
Marta Hasny, Maxime Di Folco, Keno Bressem +1
Contrastive learning methods in computer vision typically rely on augmented views of the same image or multimodal pretraining strategies that align paired modalities. However, thes…
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…
Covariance Descriptors Meet General Vision Encoders: Riemannian Deep Learning for Medical Image Classification
Josef Mayr, Anna Reithmeir, Maxime Di Folco +1
Covariance descriptors capture second-order statistics of image features. They have shown strong performance in general computer vision tasks, but remain underexplored in medical i…
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…