《Blood Vessel Segmentation in Fundus Images Based on Improved Loss Function》
一、採用U-Net網絡結構 三大優點：支持小數量的數據訓練模型；通過每個像素的分類得到更高的分割精度；訓練模型更快。 二、對比損失函數 A.Binary Cross Entropy（BCE） 當正樣本數遠小於負樣本數時（血管的像素數遠小於背景像素數，約爲1:9），模型很難分割出血管。 B.Dice Loss Dice similarity coeffcient（DSC）表示兩個輪廓區域的相似程度。
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相關文章
- 1. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
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