Adversarial learning with multi-scale loss for skin lesion segmentation
簡介 這是一個端到端的對抗神經網絡，即SegAN。generator是一個全卷積網絡，並使用了一種新的激活函數。discriminator使用一個多層 L1 L 1 損失(multi-scale L1 L 1 loss)，segmentor通過長或短範圍的像素空間關係來學習全局和局部特徵。使用的數據是ISIC2017(International Skin Imaging Collaboration
相關文章
- 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. 論文筆記《A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification》
- 6. [論文筆記] 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. 論文：Adversarial Learning for Semi-Supervised Semantic Segmentation
- 更多相關文章...
- • Swift for 循環 - Swift 教程
- • Scala for循環 - Scala教程
- • Java Agent入門實戰（三）-JVM Attach原理與使用
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 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. 論文筆記《A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification》
- 6. [論文筆記] 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. 論文：Adversarial Learning for Semi-Supervised Semantic Segmentation