Association of Grad-CAM, LIME and Multidimensional Fractal Techniques for the Classification of H&E Images

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Lopes, TRS (Author) Other; Roberto, GF (Author) Other; Carlos Soares (Author) FEUP; Tosta, TAA (Author) Other; Silva, AB (Author) Other; Loyola, AM (Author) Other; Cardoso, SV (Author) Other; de Faria, PR (Author) Other; do Nascimento, MZ (Author) Other; Neves, LA (Author) Other

Conference proceedings International

Title: VISIGRAPP (2): VISAPP

Pages: 441-447

19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2024

Rome, 27 February 2024 through 29 February 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-010-9Z1

DOI: 10.5220/0012358200003660

Abstract (EN): In this work, a method based on the use of explainable artificial intelligence techniques with multiscale and multidimensional fractal techniques is presented in order to investigate histological images stained with Hematoxylin-Eosin. The CNN GoogLeNet neural activation patterns were explored, obtained from the gradient-weighted class activation mapping and locally-interpretable model-agnostic explanation techniques. The feature vectors were generated with multiscale and multidimensional fractal techniques, specifically fractal dimension, lacunarity and percolation. The features were evaluated by ranking each entry, using the ReliefF algorithm. The discriminative power of each solution was defined via classifiers with different heuristics. The best results were obtained from LIME, with a significant increase in accuracy and AUC rates when compared to those provided by GoogLeNet. The details presented here can contribute to the development of models aimed at the classification of histological images. © 2024 by SCITEPRESS ¿ Science and Technology Publications, Lda.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6