14 papers
You Only Train Once: Differentiable Subset Selection for Omics Data
Daphné Chopard, Jorge da Silva Gonçalves, Irene Cannistraci +2
Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling.…
TOAST: Transformer Optimization using Adaptive and Simple Transformations
Irene Cannistraci, Simone Antonelli, Emanuele Palumbo +4
Foundation models achieve state-of-the-art performance across different tasks, but their size and computational demands raise concerns about accessibility and sustainability. Exist…
Foundation Model for Cardiac Time Series via Masked Latent Attention
Moritz Vandenhirtz, Samuel Ruipérez-Campillo, Simon Böhi +6
Electrocardiograms (ECGs) are among the most widely available clinical signals and play a central role in cardiovascular diagnosis. While recent foundation models (FMs) have shown…
Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography
Simon Böhi, Irene Cannistraci, Sergio Muñoz Gonzalez +8
Echocardiography is a widely used modality for cardiac assessment due to its non-invasive and cost-effective nature, but the sparse and heterogeneous spatiotemporal views of the he…
Structure is Supervision: Multiview Masked Autoencoders for Radiology
Sonia Laguna, Andrea Agostini, Alain Ryser +9
Building robust medical machine learning systems requires pretraining strategies that exploit the intrinsic structure present in clinical data. We introduce Multiview Masked Autoen…
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…