論文筆記（四）《Adversarial Networks for the Detection of Aggressive Prostate Cancer》
0 Abstract 我們提出利用一個對抗性網絡來區分專家和生成的註釋，以訓練對好的分割進行了學習參數化，因此我們假設該方法對於複雜結構的小型數據集上的分割任務具有獨特的優勢。在我們的實驗中也是如此:我們學習利用 152 名患者的 MRI 圖像來分割侵襲性前列腺癌，結果表明，在檢測侵襲性前列腺癌的靈敏度和骰子得分方面，提出的方案優於行業標準。所取得的相對增益在小數據集中表現得尤爲明顯。 1 Int
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相關文章
- 1. 論文筆記《Adversarial Networks for Prostate Cancer Detection》
- 2. False Positive Reduction Using Multiscale Contextual Features for Prostate Cancer Detection..論文筆記
- 3. 論文閱讀筆記（五十四）：V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 4. 論文筆記：Relation Networks for Object Detection
- 5. Bag of Freebies for Training Object Detection Neural Networks 論文筆記
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- 7. Dermatologist-levelclassification of skin cancer with deepneural networks論文筆記
- 8. 論文譯文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 9. 論文筆記《DermaKNet: Incorporating the knowledge of dermatologists to Convolutional Neural Networks for 》
- 10. 論文筆記：Supervised Neural Networks for the Classification of Structures