1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
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…
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…
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…