Adversarial synthesis learning enable segmentation without target modality ground truth
簡介 基於深度學習的分割有一個關鍵的限制：缺乏普遍性。通常，當用不同的成像方式分割器官或從不同的疾病組分割異常器官時，一個人會手工標註新的訓練圖像。如果一個人能夠從一種模式(例如，MRI)中重用手工標籤來訓練一種新的模式(例如，CT)的分割網絡，那麼手動的努力就可以減輕。在此之前，已有兩種階段方法被提出用於使用循環生成對抗網絡(CycleGAN)來合成目標模式的訓練圖像。然後，這些努力訓練了一個獨
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相關文章
- 1. 《Evaluating Explanation Without Ground Truth in Interpretable Machine Learning》論文總結詳述
- 2. Adversarial learning for semi-supervised semantic segmentation
- 3. Adversarial learning with multi-scale loss for skin lesion segmentation
- 4. 《Boundary and Entropy-Driven Adversarial Learning for Fundus Image Segmentation》---MICCAI2019
- 5. Generative Adversarial Text to Image Synthesis
- 6. 機器學習中ground truth的解釋
- 7. Ea-GANs: Edge-aware Generative Adversarial Networks for Cross-modality MR Image Synthesis
- 8. Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
- 9. maskrcnn裏面的ground truth來歷
- 10. 論文：Adversarial Learning for Semi-Supervised Semantic Segmentation