深度學習技術因其速度快和相對客觀的結果而被廣泛用於病理切片分析。 針對切片中的細胞核進行實例分割可以為診斷流程提供許多有用的參數，以協助並加速病理學家的診斷過程。蘇木精和曙紅染色（H&E）通常用於病理組織切片，分別以藍紫色與粉紅色強調了細胞核及細胞質，並以此進一步觀察細胞的行為和結構資訊。但是在現實世界的案例中，細胞核染色時顏色的特徵變化很大，通常取決於所使用的染料品質和染色流程，以及用於掃描數位切片的系統。因此，本研究提出一個可以連接在任意神經網絡前方的輕量級模組，以降低因染色程序差異和掃描環境不同而造成的變化導致的負面結果，進而提供更穩定的影像分割結果。值得注意的是，本研究提出的模組提高了針對沒見過的染色類型的分割性能，因此可以使擴展其實際應用的範圍。在現實案例應用中，切片可能是從多家醫院和實驗室收集而來，因此具有相當程度的染色多樣性。通過以過度的色彩增強下產生的人工染色切片影像進行的壓力測試，可以評估提出的模組之可行性。實驗結果表明，所提出的模塊可以減輕組織玻片的顏色差異造成的效能影響，並更好地協助病理學家進行相關診斷，加速分析流程。

The deep learning technique is widely used for whole slide image (WSI) analysis due to its speed and relatively objective results. Nucleus instance segmentation, in which the nuclei in the WSI are individually identified, can provide many useful parameters to assist and accelerate the diagnosis process for pathologists. Hematoxylin and eosin stain (H&E stain) is commonly used on pathology tissue slides to observe the behavior and structure information of cells. However, the color-oriented features of the nuclei vary widely depending on the particular die and staining protocol employed and also the scanning system used to obtain the digital WSIs. Accordingly, the proposed lightweight network module attached to the front of an arbitrary network can provide a more robust segmentation performance irrespective of staining protocols employed and scanning circumstances. Notably, the proposed module improves the performance for unseen color-oriented features and thus can benefit the real-world applications, where the WSIs may be collected from multiple hospitals and laboratories. The feasibility of the proposed module is evaluated by testing the network performance on immoderately color augmented input images. The experiment result shows that the proposed module can alleviate the color variance of the tissue slides and better assist the pathology for the related diagnosis.

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