Skin Lesion Classification System
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A Skin Lesion Classification System is an image classification system that can solve an automated skin lesion classification task.
References
2022
- (Tang et al., 2022) ⇒ Peng Tang, Xintong Yan, Yang Nan, Shao Xiang, Sebastian Krammer, and Tobias Lasser. (2022). “FusionM4Net: A Multi-stage Multi-modal Learning Algorithm for Multi-label Skin Lesion Classification.” Medical Image Analysis, 76.
- QUOTE: ... Skin disease is one of the most common diseases in the world. Deep learning-based methods have achieved excellent skin lesion recognition performance, most of which are based on only dermoscopy images. In recent works that use multi-modality data (patient’s meta-data, clinical images, and dermoscopy images), the methods adopt a one-stage fusion approach and only optimize the information fusion at the feature level. These methods do not use information fusion at the decision level and thus cannot fully use the data of all modalities. ...
2019
- (Zhang et al., 2019) ⇒ Jianpeng Zhang, Yutong Xie, Yong Xia, and Chunhua Shen. (2019). “Attention Residual Learning for Skin Lesion Classification.” IEEE transactions on medical imaging, 38(9).
- QUOTE: ... Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of training data, inter-class similarity, intra-class variation, and the lack of the ability to focus on semantically meaningful lesion parts. ...