Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models

Version 1 : Received: 31 December 2021 / Approved: 6 January 2022 / Online: 6 January 2022 (10:39:37 CET)

How to cite: Chatterjee, S.; Das, A.; Mandal, C.; Mukhopadhyay, B.; Vipinraj, M.; Shukla, A.; Nagaraja Rao, R.; Sarasaen, C.; Speck, O.; Nürnberger, A. TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models. Preprints 2022, 2022010072 (doi: 10.20944/preprints202201.0072.v1). Chatterjee, S.; Das, A.; Mandal, C.; Mukhopadhyay, B.; Vipinraj, M.; Shukla, A.; Nagaraja Rao, R.; Sarasaen, C.; Speck, O.; Nürnberger, A. TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models. Preprints 2022, 2022010072 (doi: 10.20944/preprints202201.0072.v1).

Abstract

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas which influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post-hoc interpretations and explanations of their models, develop more explainable tools and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-fight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods.

Keywords

Deep Learning; Black-box; Interpretability; Explainability; Model introspection; MRA segmentation

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.