Explainable image classification with improved trustworthiness for tissue characterisation
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
The deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For the surgeon to trust the model, explainability of the generated predictions needs to be provided. For image classification models, pixel attribution (PA) and risk estimation are popular methods to infer explainability. However, the former method lacks trustworthiness while the latter can not provide visual explanation of the model’s attention. In this paper, we propose the first approach which incorporates risk estimation into a PA method for improved and more trustworthy image classification explainability. The proposed method iteratively applies a classification model with a PA method to create a volume of PA maps. We introduce a method to generate an enhanced PA map by estimating the expectation values of the pixel-wise distributions. In addition, the coefficient of variation (CV) is used to estimate pixel-wise risk of this enhanced PA map. Hence, the proposed method not only provides an improved PA map but also produces an estimation of risk on the output PA values. Performance evaluation on probe-based Confocal Laser Endomicroscopy (pCLE) data verifies that our improved explainability method outperforms the state-of-the-art.
Date Issued
2023-10-01
Date Acceptance
2023-10-01
Citation
Lecture Notes in Computer Science, 2023, 14221, pp.575-585
ISBN
9783031438943
ISSN
0302-9743
Publisher
Springer Nature Switzerland
Start Page
575
End Page
585
Journal / Book Title
Lecture Notes in Computer Science
Volume
14221
Copyright Statement
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. The Version of Record is available online at: https://link.springer.com/chapter/10.1007/978-3-031-43895-0_54
Identifier
http://dx.doi.org/10.1007/978-3-031-43895-0_54
Source
MICCAI 2023
Publication Status
Published
Start Date
2023-10-08
Finish Date
2023-10-12
Coverage Spatial
Vancouver, BC, Canada
Date Publish Online
2023-10-01