Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting
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- Erscheinungsjahr:
- 2020
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- Text
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OBJECTIVE: This paper 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 that 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 [Formula: see text]. Class balancing significantly improves the mean sensitivity and we show that our diagnosis-guided loss weighting method improves the mean sensitivity by [Formula: see text] 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.
- Lizenz:
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- info:eu-repo/semantics/restrictedAccess
- Quellsystem:
- Forschungsinformationssystem des UKE
Interne Metadaten
- Quelldatensatz
- oai:pure.atira.dk:publications/27469a79-5fa4-44c1-8dfe-108df03afcd8