Coarse-to-fine feature representation based on deformable partition attention for melanoma identification

Zhang, Dong; Yang, Jing; Du, Shaoyi; Han, Hongcheng; Ge, Yuyan; Zhu, Longfei; Li, Ce; Xu, Mei; Zheng, Nanning

doi:10.1016/j.patcog.2022.109247

Cited by 3 publications

(1 citation statement)

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“…Gao et al ( 2021 ) proposed a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance and showed superior model performance on three different clinical image classification tasks. Zhang et al ( 2023 ) proposed coarse-to-fine method for melanoma and nevi recognition according to distribution of inter-class and intra-class differences as summarized by dermatologists.…”

Section: Related Workmentioning

confidence: 99%

STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition

Wang

Han

et al. 2023

Front. Neurosci.

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IntroductionThe human brain processes shape and texture information separately through different neurons in the visual system. In intelligent computer-aided imaging diagnosis, pre-trained feature extractors are commonly used in various medical image recognition methods, common pre-training datasets such as ImageNet tend to improve the texture representation of the model but make it ignore many shape features. Weak shape feature representation is disadvantageous for some tasks that focus on shape features in medical image analysis.MethodsInspired by the function of neurons in the human brain, in this paper, we proposed a shape-and-texture-biased two-stream network to enhance the shape feature representation in knowledge-guided medical image analysis. First, the two-stream network shape-biased stream and a texture-biased stream are constructed through classification and segmentation multi-task joint learning. Second, we propose pyramid-grouped convolution to enhance the texture feature representation and introduce deformable convolution to enhance the shape feature extraction. Third, we used a channel-attention-based feature selection module in shape and texture feature fusion to focus on the key features and eliminate information redundancy caused by feature fusion. Finally, aiming at the problem of model optimization difficulty caused by the imbalance in the number of benign and malignant samples in medical images, an asymmetric loss function was introduced to improve the robustness of the model.Results and conclusionWe applied our method to the melanoma recognition task on ISIC-2019 and XJTU-MM datasets, which focus on both the texture and shape of the lesions. The experimental results on dermoscopic image recognition and pathological image recognition datasets show the proposed method outperforms the compared algorithms and prove the effectiveness of our method.

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Section: Related Workmentioning

confidence: 99%