most citedFrom Pixels to Components: Eigenvector Masking for Visual Representation Learning

2 citations · 2 across the 4 of their papers we have counts for

collaborators

7 papers

cs.AI2025

Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning

Benjamin Gundersen, Nicolas Deperrois, Samuel Ruiperez-Campillo +5

Recent advances in vision-language models (VLMs) have improved Chest X-ray (CXR) interpretation in multiple aspects. However, many medical VLMs rely solely on supervised fine-tunin…

eess.IV2025

Temporal Representation Learning for Real-Time Ultrasound Analysis

Yves Stebler, Thomas M. Sutter, Ece Ozkan +1

Ultrasound (US) imaging is a critical tool in medical diagnostics, offering real-time visualization of physiological processes. One of its major advantages is its ability to captur…

eess.IV2025

From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound

Max Krähenmann, Sergio Tascon-Morales, Fabian Laumer +2

Volumetric ultrasound has the potential to significantly improve diagnostic accuracy and clinical decision-making, yet its widespread adoption remains limited by dependence on spec…

eess.IV2025

Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach

Lucas Erlacher, Samuel Ruipérez-Campillo, Holger Michel +4

Pulmonary hypertension (PH) in newborns is a critical condition characterized by elevated pressure in the pulmonary arteries, leading to right ventricular strain and heart failure.…

eess.SP2025

A Denoising VAE for Intracardiac Time Series in Ischemic Cardiomyopathy

Samuel Ruipérez-Campillo, Alain Ryser, Thomas M. Sutter +10

In the field of cardiac electrophysiology (EP), effectively reducing noise in intra-cardiac signals is crucial for the accurate diagnosis and treatment of arrhythmias and cardiomyo…

cs.LG20252 cited

From Pixels to Components: Eigenvector Masking for Visual Representation Learning

Alice Bizeul, Thomas Sutter, Alain Ryser +3

Predicting masked from visible parts of an image is a powerful self-supervised approach for visual representation learning. However, the common practice of masking random patches o…