3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes-筆記
網絡結構： 網絡改進的地方是通過反捲積（核大小爲3X3X3，步長爲2）中間層，使得和輸入大小相同，然後通過label計算loss，和最終loss一起進行梯度更新。中間反捲積層輸出的loss可以防止梯消失。（深度監督DSN） 由於最終輸出是通過反捲積得到的，只通過閾值生成結果，有時後邊緣會不精確。所以在橫斷面又使用了CRF。 觀察了中間層的輸出：證明該論文的中間層學習到的表達相關性較小（特徵
相關文章
- 1. 【論文筆記】——FEATURE FUSION ENCODER DECODER NETWORK FOR AUTOMATIC LIVER LESION SEGMENTATION
- 2. 【論文閱讀筆記】Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Net
- 3. 醫學分割論文:Automatic Liver and Lesion Segmentation in CT Using Cascaded FCNN and 3D-CRF
- 4. Automatic Tumor Segmentation from MRI scans 閱讀筆記
- 5. Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
- 6. DSOD: Learning Deeply Supervised Object Detectors from Scratch
- 7. Reading Note: DSOD: Learning Deeply Supervised Object Detectors from Scratch
- 8. DSOD: Learning Deeply Supervised Object Detectors from Scratch 論文筆記
- 9. 《Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation》筆記
- 10. DSOD： Learning Deeply Supervised Object Detectors from Scratch
- 更多相關文章...
- • ASP.NET Razor - 標記 - ASP.NET 教程
- • Scala for循環 - Scala教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • 互聯網組織的未來：剖析GitHub員工的任性之源
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. 【論文筆記】——FEATURE FUSION ENCODER DECODER NETWORK FOR AUTOMATIC LIVER LESION SEGMENTATION
- 2. 【論文閱讀筆記】Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Net
- 3. 醫學分割論文:Automatic Liver and Lesion Segmentation in CT Using Cascaded FCNN and 3D-CRF
- 4. Automatic Tumor Segmentation from MRI scans 閱讀筆記
- 5. Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
- 6. DSOD: Learning Deeply Supervised Object Detectors from Scratch
- 7. Reading Note: DSOD: Learning Deeply Supervised Object Detectors from Scratch
- 8. DSOD: Learning Deeply Supervised Object Detectors from Scratch 論文筆記
- 9. 《Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation》筆記
- 10. DSOD： Learning Deeply Supervised Object Detectors from Scratch