Multitask Classification of Breast Cancer Pathological Images Using SE-DenseNet
摘要： 本文采用密集連接的卷積網絡(DenseNet)結構，吸收SeNet，對一組蘇木精和伊紅(H&E)染色乳腺組織學顯微鏡 Camelyon16 進行多任務分類。全幻燈片圖像（WSI）通常存儲在多分辨率金字塔中，我們的數據集包含Camelyon16 在*5、*20、*40，三倍放大率下的patches。 我們的多任務是通過連接同一網絡末端的兩個分類器來識別patches的放大率並區分提取的pat
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相關文章
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