@article{8710336,
Author = {N. Gessert and T. Sentker and F. Madesta and R. Schmitz and H. Kniep and I. Baltruschat and R. Werner and A. Schlaefer},
Title = {Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting.},
Journal = {<em>IEEE Transactions on Biomedical Engineering</em>.},
Year = {(2019).},
Pages = {Accepted},
Doi = {10.1109/TBME.2019.2915839},
Url = {https://arxiv.org/abs/1905.02793},
Keywords = {Lesions;Skin;Computer architecture;Medical diagnostic imaging;Image resolution;Sensitivity;Skin Lesion Classification;Deep Learning;Attention;Dermoscopy},
Abstract = {Objective: This work addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high class imbalance encountered in real-world multi-class datasets. Methods: To use high-resolution images, we propose a novel patch-based attention architecture that provides global context between small, high-resolution patches. We modify three pretrained architectures and study the performance of patch-based attention. To counter class imbalance problems, we compare oversampling, balanced batch sampling, and class-specific loss weighting. Additionally, we propose a novel diagnosis-guided loss weighting method which takes the method used for ground-truth annotation into account. Results: Our patch-based attention mechanism outperforms previous methods and improves the mean sensitivity by 7%. Class balancing significantly improves the mean sensitivity and we show that our diagnosis-guided loss weighting method improves the mean sensitivity by 3% over normal loss balancing. Conclusion: The novel patch-based attention mechanism can be integrated into pretrained architectures and provides global context between local patches while outperforming other patch-based methods. Hence, pretrained architectures can be readily used with high resolution images without downsampling. The new diagnosis-guided loss weighting method outperforms other methods and allows for effective training when facing class imbalance. Significance: The proposed methods improve automatic skin lesion classification. They can be extended to other clinical applications where high-resolution image data and class imbalance are relevant.}
}

@COMMENT{Bibtex file generated on 2026-5-12 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications/2019-2013 }