Explaining chest x-ray pathologies in natural language
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work,...
Hoofdauteurs: | Kayser, M, Emde, C, Camburu, OM, Parsons, G, Papiez, B, Lukasiewicz, T |
---|---|
Formaat: | Conference item |
Taal: | English |
Gepubliceerd in: |
Springer
2022
|
Gelijkaardige items
-
e-ViL: A dataset and benchmark for natural language explanations in vision-language tasks
door: Kayser, M, et al.
Gepubliceerd in: (2022) -
e-SNLI: Natural language inference with natural language explanations
door: Camburu, O, et al.
Gepubliceerd in: (2018) -
Explaining deep neural networks
door: Camburu, OM
Gepubliceerd in: (2020) -
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
door: Camburu, O-M, et al.
Gepubliceerd in: (2020) -
The gap on GAP: tackling the problem of differing data distributions in bias−measuring datasets
door: Kocijan, V, et al.
Gepubliceerd in: (2021)