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Found 3 papers, 3 papers with code
Explaining Chest X-ray Pathologies in Natural Language
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice.
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
e-ViL is a benchmark for explainable vision-language tasks that establishes a unified evaluation framework and provides the first comprehensive comparison of existing approaches that generate NLEs for VL tasks.
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
We then investigated different strategies, such as a learning without forgetting framework, to leverage artifact knowledge to improve automated polyp detection.
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