Efficient Deep Learning Ensemble for Skin Lesion Classification

David Gaviria, Md Saker, Petia Radeva, Petia Radeva

2023

Abstract

Vision Transformers (ViTs) are deep learning techniques that have been gaining in popularity in recent years. In this work, we study the performance of ViTs and Convolutional Neural Networks (CNNs) on skin lesions classification tasks, specifically melanoma diagnosis. We show that regardless of the performance of both architectures, an ensemble of them can improve their generalization. We also present an adaptation to the Gram-OOD* method (detecting Out-of-distribution (OOD) using Gram matrices) for skin lesion images. Moreover, the integration of super-convergence was critical to success in building models with strict computing and training time constraints. We evaluated our ensemble of ViTs and CNNs, demonstrating that generalization is enhanced by placing first in the 2019 and third in the 2020 ISIC Challenge Live Leaderboards (available at https://challenge.isic-archive.com/leaderboards/live/).

Paper Citation

in Harvard Style

Gaviria D., Saker M. and Radeva P. (2023). Efficient Deep Learning Ensemble for Skin Lesion Classification. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 303-314. DOI: 10.5220/0011816100003417

in Bibtex Style

@conference{visapp23,
author={David Gaviria and Md Saker and Petia Radeva},
title={Efficient Deep Learning Ensemble for Skin Lesion Classification},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={303-314},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011816100003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Efficient Deep Learning Ensemble for Skin Lesion Classification
SN - 978-989-758-634-7
AU - Gaviria D.
AU - Saker M.
AU - Radeva P.
PY - 2023
SP - 303
EP - 314
DO - 10.5220/0011816100003417
PB - SciTePress