Learning Visual Context by Comparison
arXiv:2007.07506
Abstract
Finding diseases from an X-ray image is an important yet highly challenging task. Current methods for solving this task exploit various characteristics of the chest X-ray image, but one of the most important characteristics is still missing: the necessity of comparison between related regions in an image. In this paper, we present Attend-and-Compare Module (ACM) for capturing the difference between an object of interest and its corresponding context. We show that explicit difference modeling can be very helpful in tasks that require direct comparison between locations from afar. This module can be plugged into existing deep learning models. For evaluation, we apply our module to three chest X-ray recognition tasks and COCO object detection & segmentation tasks and observe consistent improvements across tasks. The code is available at https://github.com/mk-minchul/attend-and-compare.
arXiv commentECCV 2020 spotlight paper
References in corpus (4)
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- PadChest: A large chest x-ray image dataset with multi-label annotated reports
- ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
- Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions