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Found 4 papers, 4 papers with code
The Effect of Counterfactuals on Reading Chest X-rays
This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays.
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0. 15$\pm$0. 95 in a 5 point scale with p=0. 01) with only a small increase in false positive predictions (0. 04$\pm$1. 06 with p=0. 57).
RPNet: an End-to-End Network for Relative Camera Pose Estimation
While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way.
TS-Net: Combining modality specific and common features for multimodal patch matching
Multimodal patch matching addresses the problem of finding the correspondences between image patches from two different modalities, e. g. RGB vs sketch or RGB vs near-infrared.
