8 citations · 9 across the 7 of their papers we have counts for
8 papers
Refining Cytology Predictions with Conditional Random Fields
Manon Dausort, Tiffanie Godelaine, Karim El Khoury +3
Vision-language models (VLMs) achieve strong zero-shot (ZS) classification on histology images but do not perform as well on cytology, whose stains and cell morphology differ marke…
Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols
Tiffanie Godelaine, Maxime Zanella, Karim El Khoury +2
Automating the analysis of whole-slide images has high clinical value, since characterizing cancers requires examining them in detail. Such analysis increasingly relies on vision-l…
Conditional Random Fields for Interactive Refinement of Histopathological Predictions
Tiffanie Godelaine, Maxime Zanella, Karim El Khoury +3
Assisting pathologists in the analysis of histopathological images has high clinical value, as it supports cancer detection and staging. In this context, histology foundation model…
Leveraging Prediction Entropy for Automatic Prompt Weighting in Zero-Shot Audio-Language Classification
Karim El Khoury, Maxime Zanella, Tiffanie Godelaine +2
Audio-language models have recently demonstrated strong zero-shot capabilities by leveraging natural-language supervision to classify audio events without labeled training data. Ye…
Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models
Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer +1
Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However,…
Exploring Foundation Models Fine-Tuning for Cytology Classification
Manon Dausort, Tiffanie Godelaine, Maxime Zanella +3
Cytology slides are essential tools in diagnosing and staging cancer, but their analysis is time-consuming and costly. Foundation models have shown great potential to assist in the…
Physically Interpretable Probabilistic Domain Characterization
Anaïs Halin, Sébastien Piérard, Renaud Vandeghen +10
Characterizing domains is essential for models analyzing dynamic environments, as it allows them to adapt to evolving conditions or to hand the task over to backup systems when fac…
Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillance
Sébastien Piérard, Anthony Cioppa, Anaïs Halin +5
Various tasks encountered in real-world surveillance can be addressed by determining posteriors (e.g. by Bayesian inference or machine learning), based on which critical decisions…