20 citations · 34 across the 9 of their papers we have counts for
10 papers
VICCA: Visual Interpretation and Comprehension of Chest X-ray Anomalies in Generated Report Without Human Feedback
Sayeh Gholipour Picha, Dawood Al Chanti, Alice Caplier
As artificial intelligence (AI) becomes increasingly central to healthcare, the demand for explainable and trustworthy models is paramount. Current report generation systems for ch…
Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric
Sayeh Gholipour Picha, Dawood Al Chanti, Alice Caplier
Medical language processing and deep learning techniques have emerged as critical tools for improving healthcare, particularly in the analysis of medical imaging and medical text d…
How far generated data can impact Neural Networks performance?
Sayeh Gholipour Picha, Dawood AL Chanti, Alice Caplier
The success of deep learning models depends on the size and quality of the dataset to solve certain tasks. Here, we explore how far generated data can aid real data in improving th…
Using Synthetic Corruptions to Measure Robustness to Natural Distribution Shifts
Alfred Laugros, Alice Caplier, Matthieu Ospici
Synthetic corruptions gathered into a benchmark are frequently used to measure neural network robustness to distribution shifts. However, robustness to synthetic corruption benchma…
Using the Overlapping Score to Improve Corruption Benchmarks
Alfred Laugros, Alice Caplier, Matthieu Ospici
Neural Networks are sensitive to various corruptions that usually occur in real-world applications such as blurs, noises, low-lighting conditions, etc. To estimate the robustness o…
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training
Alfred Laugros, Alice Caplier, Matthieu Ospici
Despite their performance, Artificial Neural Networks are not reliable enough for most of industrial applications. They are sensitive to noises, rotations, blurs and adversarial ex…