4 citations · 4 across the 6 of their papers we have counts for
8 papers
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…
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…
CIA: Controllable Image Augmentation Framework Based on Stable Diffusion
Mohamed Benkedadra, Dany Rimez, Tiffanie Godelaine +6
Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeli…
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…