5 papers
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…
Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations
Maxime Di Folco, Gabriel Bernardino, Patrick Clarysse +1
Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations in how input descriptors are def…
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
Deniz Daum, Richard Osuala, Anneliese Riess +3
Generally, the small size of public medical imaging datasets coupled with stringent privacy concerns, hampers the advancement of data-hungry deep learning models in medical imaging…
Influence of Prompting Strategies on Segment Anything Model (SAM) for Short-axis Cardiac MRI segmentation
Josh Stein, Maxime Di Folco, Julia A. Schnabel
The Segment Anything Model (SAM) has recently emerged as a significant breakthrough in foundation models, demonstrating remarkable zero-shot performance in object segmentation task…
Sparse annotation strategies for segmentation of short axis cardiac MRI
Josh Stein, Maxime Di Folco, Julia Schnabel
Short axis cardiac MRI segmentation is a well-researched topic, with excellent results achieved by state-of-the-art models in a supervised setting. However, annotating MRI volumes…