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Learning To Pay Attention To Mistakes

Xu, M-C; Oxtoby, N; Alexander, DC; Jacob, J; (2020) Learning To Pay Attention To Mistakes. In: (Proceedings) The 31st British Machine Vision Virtual Conference. Green open access

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Abstract

In convolutional neural network based medical image segmentation, the periphery of foreground regions representing malignant tissues may be disproportionately assigned as belonging to the background class of healthy tissues [18][21][24][12][4]. Misclassification of foreground pixels as the background class can lead to high false negative detection rates. In this paper, we propose a novel attention mechanism to directly address such high false negative rates, called Paying Attention to Mistakes. Our attention mechanism steers the models towards false positive identification, which counters the existing bias towards false negatives. The proposed mechanism has two complementary implementations: (a) “explicit” steering of the model to attend to a larger Effective Receptive Field on the foreground areas; (b) “implicit” steering towards false positives, by attending to a smaller Effective Receptive Field on the background areas. We validated our methods on three tasks: 1) binary dense prediction between vehicles and the background using CityScapes; 2) Enhanced Tumour Core segmentation with multi-modal MRI scans in BRATS2018; 3) segmenting stroke lesions using ultrasound images in ISLES2018. We compared our methods with state-of-the-art attention mechanisms in medical imaging, including self-attention, spatial-attention and spatial-channel mixed attention. Across all of the three different tasks, our models consistently outperform the baseline models in Intersection over Union (IoU) and/or Hausdorff Distance (HD). For instance, in the second task, the “explicit” implementation of our mechanism reduces the HD of the best baseline by more than 26%, whilst improving the IoU by more than 3%. We believe our proposed attention mechanism can benefit a wide range of medical and computer vision tasks, which suffer from over-detection of background.

Type: Proceedings paper
Title: Learning To Pay Attention To Mistakes
Event: The 31st British Machine Vision Virtual Conference
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.bmvc2020-conference.com/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10109237
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