Adversarial learning with multi-scale loss for skin lesion segmentation
简介 这是一个端到端的对抗神经网络，即SegAN。generator是一个全卷积网络，并使用了一种新的激活函数。discriminator使用一个多层 L1 L 1 损失(multi-scale L1 L 1 loss)，segmentor通过长或短范围的像素空间关系来学习全局和局部特征。使用的数据是ISIC2017(International Skin Imaging Collaboration
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相关文章
- 1. Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
- 2. Automatic skin lesion segmentation with fully convolutional-deconvolutional networks
- 3. A MULTI-TASK FRAMEWORK WITH FEATURE PASSING MODULE FOR SKIN LESION CLASSIFICATION AND SEGMENTATION
- 4. Adversarial learning for semi-supervised semantic segmentation
- 5. 论文笔记《A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification》
- 6. [论文笔记] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- 7. DermoNet: Densely Linked Convolutional Neural Network for Efficient Skin Lesion Segmentation
- 8. Review of Semantic Segmentation with Deep Learning
- 9. Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
- 10. 论文：Adversarial Learning for Semi-Supervised Semantic Segmentation