Applying Layer-Wise Relevance Propagation on U-Net Architectures

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

For safety critical applications, it is still a challenge to use AI and fulfill all regulatory requirements. Medicine/healthcare and transportation are two fields where regulatory requirements are of fundamental importance. A wrong decision can lead to serious hazards or even deaths. In these fields, semantic segmentation is often utilized to extract features. Especially U-Net architectures are used. This paper shows how to apply layer-wise relevance propagation (LRP) to a trained U-Net architecture. We achieve an efficient explanation of a segmentation by back-propagating the whole resulting image. To tackle the non-linear results of the LRP, we introduce a threshold mechanism in combination with a logarithmic transfer function to preprocess the data for visualization. We demonstrate our method on three use cases: the segmentation of a fiber-reinforced polymer in the field of non-destructive testing, the segmentation of pedestrians in an automotive application, and a lung segmentation example from the medical domain.

Original languageEnglish
Title of host publicationPattern Recognition - 27th International Conference, ICPR 2024, Proceedings
EditorsApostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal
PublisherSpringer
Pages106-121
Number of pages16
ISBN (Print)9783031781971
DOIs
Publication statusPublished - 2025
Event27th International Conference on Pattern Recognition, ICPR 2024 - Kolkata, India
Duration: 1 Dec 20245 Dec 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15312 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Pattern Recognition, ICPR 2024
Country/TerritoryIndia
CityKolkata
Period01.12.202405.12.2024

Keywords

  • Explainable AI
  • Layerwise Relevance Propagation
  • Segmentation
  • U-Net

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