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Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images by Tang, Peng; Nan, Yang; Lasser, Tobias is a scholarly article available to read on EtoBox.

What is Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images about?

Many skin lesion analysis (SLA) methods recently focused on developing a multi-modal-based multi-label classification method due to two factors. The first is multi-modal data, i.e., clinical and dermoscopy images, which can provide complementary information to obtain more accurate results than single-modal data. The second one is that multi-label classification, i.e., seven-point checklist (SPC) criteria as an auxiliary classification task can not only boost the diagnostic accuracy of melanoma in the deep learning (DL) pipeline but also provide more useful functions to the clinical doctor as it is commonly used in clinical dermatologist's diagnosis. However, most methods only focus on designing a better module for multi-modal data fusion; few methods explore utilizing the label correlation between SPC and skin disease for performance improvement. This study fills the gap that introduces a Graph Convolution Network (GCN) to exploit prior co-occurrence between each category as a correlation matrix into the DL model for the multi-label classification. However, directly applying GCN degraded the performances in our experiments; we attribute this to the weak generalization ability of GC

Author

Tang, Peng; Nan, Yang; Lasser, Tobias

Published

2023

Language

EN