Gessert, Nils ThorbenNils ThorbenGessertSentker, ThiloThiloSentkerMadesta, FredericFredericMadestaSchmitz, RüdigerRüdigerSchmitzKniep, HelgeHelgeKniepBaltruschat, Ivo-MatteoIvo-MatteoBaltruschatWerner, RenéRenéWernerSchlaefer, AlexanderAlexanderSchlaefer2020-01-082020-01-082020-02IEEE Transactions on Biomedical Engineering 67 (2): 8710336 (2020-02)http://hdl.handle.net/11420/4295OBJECTIVE: 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.en0018-929420202495503Allgemeines, WissenschaftJournal Article10.1109/TBME.2019.2915839Other