Explainable Model for Localization of Spiculation in Lung Nodules

Mirtha Lucas*, Miguel Lerma, Jacob Furst, Daniela Raicu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


When determining a lung nodule malignancy one must consider the spiculation represented by spike-like structures in the nodule’s boundary. In this paper, we develop a deep learning model based on a VGG16 architecture to locate the presence of spiculation in lung nodules from Computed Tomography images. In order to increase the expert’s confidence in the model output, we apply our novel Riemann-Stieltjes Integrated Gradient-weighted Class Activation Mapping attribution method to visualize areas of the image (spicules). Therefore, the attribution method is applied to the layer of the model that is responsible for the detection of the spiculation features. We show that the first layers of the network are specialized in detecting low-level features such as edges, the last convolutional layer detects the general area occupied by the nodule, and finally, we identify that spiculation structures are detected at an intermediate layer. We use three different metrics to support our findings.

Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2022 Workshops, Proceedings
EditorsLeonid Karlinsky, Tomer Michaeli, Ko Nishino
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783031250811
StatePublished - 2023
Event17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Duration: Oct 23 2022Oct 27 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13807 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th European Conference on Computer Vision, ECCV 2022
CityTel Aviv


  • Artificial intelligence
  • CAD
  • Computer-aided detection
  • Imaging informatics
  • XAI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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