4 citations · 5 across the 10 of their papers we have counts for
12 papers
Exploiting Spatial Structure for Transductive Few-Shot Classification of Whole-Slide Images
Tiffanie Godelaine, Manon Dausort, Karim El Khoury +3
Automating the analysis of whole-slide images (WSIs), a key step in cancer diagnosis, has high clinical value, as it can reduce pathologist's workload while improving diagnosis acc…
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…
Locally Consistent Transductive Information Maximization for Few-Shot Remote Sensing Scene Classification
Karim El Khoury, Benoît Gérin, Benoît Macq +1
Remote sensing scene classification is increasingly relying on foundation models pre-trained on large-scale Earth-observation data. Moreover, transductive inference, which exploits…
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,…
Optimizing Resources for On-the-Fly Label Estimation with Multiple Unknown Medical Experts
Tim Bary, Tiffanie Godelaine, Axel Abels +1
Accurate ground truth estimation in medical screening programs often relies on coalitions of experts and peer second opinions. Algorithms that efficiently aggregate noisy annotatio…
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…
Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification
Karim El Khoury, Maxime Zanella, Benoît Gérin +5
Vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification metho…
Streamlined Hybrid Annotation Framework using Scalable Codestream for Bandwidth-Restricted UAV Object Detection
Karim El Khoury, Tiffanie Godelaine, Simon Delvaux +2
Emergency response missions depend on the fast relay of visual information, a task to which unmanned aerial vehicles are well adapted. However, the effective use of unmanned aerial…