Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration
一、Motivation 1. 傳統的配準方法試圖基於基於強度的相似性度量來優化變形場。這些方法通常涉及計算量大的高維優化和與任務有關的參數調整。 2. 最近的基於CNN的有監督配準方法雖然能夠一定程度上解決傳統方法的缺點，但是受限於ground-truth的真實性。 3. 在無監督的CNN方法利用相似性指標作爲損失函數的一部分，這些相似性指標與圖像的性質密切相關，可能不適用於處理各種數據集。
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相關文章
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