The Effect of Counterfactuals on Reading Chest X-rays

Autor:	Cohen, Joseph Paul, Brooks, Rupert, En, Sovann, Zucker, Evan, Pareek, Anuj, Lungren, Matthew, Chaudhari, Akshay
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays. We conduct a reader study with two radiologists assessing 240 chest X-ray predictions to rate their confidence that the model's prediction is correct using a 5 point scale. Half of the predictions are false positives. Each prediction is explained twice, once using traditional attribution methods and once with a counterfactual explanation. The overall results indicate that counterfactual explanations allow a radiologist to have more confidence in true positive predictions compared to traditional approaches (0.15$\pm$0.95 with p=0.01) with only a small increase in false positive predictions (0.04$\pm$1.06 with p=0.57). We observe the specific prediction tasks of Mass and Atelectasis appear to benefit the most compared to other tasks. Comment: Abstract submitted to CVPR XAI4CV 2023 based on longer version: arXiv:2102.09475
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.00487 Zobrazit plný text záznamu View this record from Arxiv