Accurate skin lesion diagnosis requires a great effort from experts to identify the characteristics from clinical and dermoscopic images. Deep multimodal learning-based methods can reduce intra- and inter-reader variability and improve diagnostic accuracy compared to the single modality-based methods. This study develops a novel method, named adversarial multimodal fusion with attention mechanism (AMFAM), to perform multimodal skin lesion classification. Specifically, we adopt a discriminator that uses adversarial learning to enforce the feature extractor to learn the correlated information explicitly. Moreover, we design an attention-based reconstruction strategy to encourage the feature extractor to concentrate on learning the features of the lesion area, thus, enhancing the feature vector from each modality with more discriminative information. Unlike existing multimodal-based approaches, which only focus on learning complementary features from dermoscopic and clinical images, our method considers both correlated and complementary information of the two modalities for multimodal fusion. To verify the effectiveness of our method, we conduct comprehensive experiments on a publicly available multimodal and multi-task skin lesion classification dataset: 7-point criteria evaluation database. The experimental results demonstrate that our proposed method outperforms the current state-of-the-art methods and improves the average AUC score by above 2% on the test set.
Keywords: Correlated and complementary information; Multimodal fusion; Multimodal learning; Skin lesion classification.
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