论文标题
基于U-NET的皮肤病变细分模型：更多的关注和增强
U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation
论文作者
论文摘要
根据WHO [1]，自1970年代以来，对黑色素瘤皮肤癌的诊断一直更加频繁。但是，如果早点发现，黑色素瘤的5年生存率可能会增加到99％。在这方面，皮肤病变细分在监测和治疗计划中可能是关键的。在这项工作中，在ISIC 2016数据集中对十种模型和四种增强配置进行了培训。使用五个指标比较了性能和过度拟合。我们的结果表明，U-NET-RESNET50和R2U-NET具有最高的度量值，以及两个数据增强方案。我们还研究了U-NET体系结构中的CBAM和AG块，从而以微薄的计算成本增强了细分性能。此外，我们建议按顺序使用金字塔，Ag和CBAM块，这显着超过了两者单独使用的结果。最后，我们的实验表明，利用注意力模块的模型成功克服了常见的皮肤病变分割问题。最后，本着可重复的研究精神，我们公开实施模型和代码。
According to WHO[1], since the 1970s, diagnosis of melanoma skin cancer has been more frequent. However, if detected early, the 5-year survival rate for melanoma can increase to 99 percent. In this regard, skin lesion segmentation can be pivotal in monitoring and treatment planning. In this work, ten models and four augmentation configurations are trained on the ISIC 2016 dataset. The performance and overfitting are compared utilizing five metrics. Our results show that the U-Net-Resnet50 and the R2U-Net have the highest metrics value, along with two data augmentation scenarios. We also investigate CBAM and AG blocks in the U-Net architecture, which enhances segmentation performance at a meager computational cost. In addition, we propose using pyramid, AG, and CBAM blocks in a sequence, which significantly surpasses the results of using the two individually. Finally, our experiments show that models that have exploited attention modules successfully overcome common skin lesion segmentation problems. Lastly, in the spirit of reproducible research, we implement models and codes publicly available.
