In some medical imaging tasks and other settings where only small parts of
the image are informative for the classification task, traditional CNNs can
sometimes struggle to generalise. Manually annotated Regions of Interest (ROI)
are sometimes used to isolate the most informative parts of the image. However,
these are expensive to collect and may vary significantly across annotators. To
overcome these issues, we propose a framework that employs saliency maps to
obtain soft spatial attention masks that modulate the image features at
different scales. We refer to our method as Adversarial Counterfactual
Attention (ACAT). ACAT increases the baseline classification accuracy of
lesions in brain CT scans from 71.39% to 72.55% and of COVID-19 related
findings in lung CT scans from 67.71% to 70.84% and exceeds the performance of
competing methods. We investigate the best way to generate the saliency maps
employed in our architecture and propose a way to obtain them from
adversarially generated counterfactual images. They are able to isolate the
area of interest in brain and lung CT scans without using any manual
annotations. In the task of localising the lesion location out of 6 possible
regions, they obtain a score of 65.05% on brain CT scans, improving the score
of 61.29% obtained with the best competing method.

本文提出了一种基于显著性图的 Adversarial Counterfactual Attention（ACAT）框架，可以没有手动注释地分隔医学影像中感兴趣区域，显著提高了病灶 CT 扫描和肺部 COVID-19 相关发现的分类精度。