1 citations · 1 across the 4 of their papers we have counts for
5 papers
CoralBay: A Self-Supervised CT Foundation Model
Ioannis Gatopoulos, Nicolas Känzig, Sebastian Otálora +1
Self-supervised learning has enabled large-scale pre-training on 2D natural images, producing general-purpose visual representations that transfer effectively across tasks. However…
XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography
Haozhe Luo, Shelley Zixin Shu, Ziyu Zhou +2
Vision-language models (VLMs) have recently shown remarkable zero-shot performance in medical image understanding, yet their grounding ability, the extent to which textual concepts…
Training state-of-the-art pathology foundation models with orders of magnitude less data
Mikhail Karasikov, Joost van Doorn, Nicolas Känzig +5
The field of computational pathology has recently seen rapid advances driven by the development of modern vision foundation models (FMs), typically trained on vast collections of p…
H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression
Niccoló Marini, Manfredo Atzori, Sebastian Otálora +2
Computational pathology is a domain that aims to develop algorithms to automatically analyze large digitized histopathology images, called whole slide images (WSI). WSIs are produc…
DeepFloat: Resource-Efficient Dynamic Management of Vehicular Floating Content
Gaetano Manzo, Sebastian Otalora, Marco Ajmone Marsan +3
Opportunistic communications are expected to playa crucial role in enabling context-aware vehicular services. A widely investigated opportunistic communication paradigm for storing…