骨髓檢查是診斷血液疾病中最重要的指標之一，通常在顯微鏡下進行，使用油浸物鏡頭和總放大倍率為100× 的目鏡。另一方面，有絲分裂的檢測和鑑定對於準確的癌症診斷和分級以及預測治療成功和存活率至關重要。完全自動化的骨髓檢查和有絲分裂圖像檢查在全幅切片圖像上的應用需求很高，但具有挑戰性且研究較少。
首先，顯微鏡圖像檢查的複雜性和差異性不良是由於細胞類型的多樣性，多類型細胞成熟過程中微妙的內部差異，細胞重疊，脂質干擾和染色差異所致。其次，對於全幅切片圖像的手動標記是耗時又費力的，且易受觀察者內部變異性的影響，這導致監督信息僅限於由人類註釋的有限、易識別且分散的細胞。
第三，當訓練數據稀疏標記時，許多未標記的感興趣對象被錯誤定義為背景，這嚴重困惑了人工智能學習者。本文提出了一種高效且完全自動的CW-Net方法，用於解決上述三個問題，並展示了它在骨髓檢查和有絲分裂圖像檢查中的優越性能。實驗結果顯示，所提出的CW-Net 在一個包含19 種骨髓細胞類型的大
型骨髓WSI 數據集（共有16,456 個註釋細胞）和一個用於有絲分裂圖像評估的大規模WSI 數據集（共有262,481 個註釋細胞，包含五種細胞類型）上具有強韌性和泛化能力。
基於CW-Net 建立一個活體細胞的追蹤演算法，利用此演算法提高細胞變異的診斷準確率，以及降低活體細胞追蹤的人力，提高診斷的效率。透過活體細胞的影像追蹤數據結果以驗證此演算法的有效性及準確性。

Bone marrow examination is one of the most important indicators in diagnosing hematologic disorders and is typically performed under the microscope via oil-immersion objective lens with a total 100X objective magnification. On the other hand, mitotic detection and identification is critical not only for accurate cancer diagnosis and grading but also for predicting therapy success and survival.Fully automated bone marrow examination and mitotic figure examination from whole-slide images is highly demanded but challenging and poorly explored. Firstly, the complexity and poor reproducibility of microscopic image examination are due to the cell type diversity, delicate intra-lineage discrepancy within the multi-type cell maturation process, cells overlapping, lipid interference and stain variation.
Secondly, manual annotation on whole-slide images is tedious, laborious and subject to intra-observer variability, which causes the supervised information restricted to limited, easily identifiable and scattered cells annotated by humans.

Thirdly, when the training data is sparsely labeled, many unlabeled objects of interest are wrongly defined as background, which severely confuses AI learners.This paper presents an efficient and fully automatic CW-Net approach to address the three issues mentioned above and demonstrates its superior performance on both bone marrow examination and mitotic figure examination. The experimental results demonstrate the robustness and generalizability of the proposed CW-Net on a large bone marrow WSI dataset with 16,456 annotated cells of 19 bone marrow cell types and a large-scale WSI dataset for mitotic figure assessment with 262,481 annotated cells of five cell types.

Establish a live cell tracking algorithm based on CW-Net to improve the diagnostic accuracy of cell variations and reduce the manpower required for live cell tracking, thereby enhancing diagnostic efficiency. Validate the effectiveness and accuracy of this algorithm through the results of live cell imaging tracking data.

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