本文選題：細胞核異型性 + 深度卷積網絡��； 參考：《中國生物醫(yī)學工程學報》2017年03期

【摘要】：細胞核異型性是評估乳腺癌惡性程度的一個重要指標,主要體現(xiàn)在細胞核的形狀、大小變化、紋理和質密度不均化。提出基于深度學習和結合策略模型的乳腺組織細胞核異型性自動評分模型。該模型使用3個卷積神經網絡,分別處理每個病例的3種不同分辨率下的組織病理圖像,每個網絡結合滑動窗口和絕對多數(shù)投票法,評估每個病例同一種分辨率下的圖像的分值,得到3種分辨率下的評分結果。使用相對多數(shù)投票法,綜合評估每個病例的最終細胞核異型性評分結果。為評估模型對細胞核異型性評分的有效性,利用訓練好的模型對124個病例的測試圖像進行自動評分,并把其評分結果與病理醫(yī)生的評分結果作比較,進行性能評估。該模型的評分正確率得分為67分,其結果在現(xiàn)有的細胞核異型性評分模型中準確率排名第二。此外,該模型的計算效率也很高,平均在每張×10、×20、×40分辨率下圖像的計算時間分別約為1.2、5.5、30 s。研究表明,該細胞核異型性評分模型不僅具有較高的準確性,而且計算效率高,因此具備潛在的臨床應用能力。
[Abstract]:Nuclear heterogeneity is an important index to evaluate the malignancy of breast cancer, which is mainly reflected in the shape, size, texture and density of the nucleus. An automatic grading model of breast tissue nucleus heterogeneity based on deep learning and combined strategy model was proposed. The model uses three convolution neural networks to process three different resolution histopathological images of each case. Each network combines sliding window and absolute majority voting. The image scores of each case at the same resolution were evaluated and the scoring results were obtained under three resolutions. A relative majority voting method was used to evaluate the final nuclear heterogeneity score for each case. In order to evaluate the effectiveness of the model in evaluating nuclear heterogeneity, the trained model was used to evaluate the image of 124 cases automatically, and the results were compared with those of pathologist to evaluate their performance. The scoring accuracy of this model is 67, and the result is the second in the current nuclear heterogeneity scoring model. In addition, the computational efficiency of the model is also very high, and the average computing time of each image at the resolution of 10 脳 10, 脳 20, 脳 40 is about 1.2 ~ 5.5 ~ 30 s, respectively. The results show that the model not only has high accuracy, but also has high computational efficiency, so it has potential clinical application ability.
【作者單位】： 南京信息工程大學江蘇省大數(shù)據分析技術重點實驗室;華中科技大學附屬武漢市中心醫(yī)院病理科;
【基金】：國家自然科學基金(61273259) 江蘇省“六大人才高峰”高層次人才項目(2013-XXRJ-019) 江蘇省自然科學基金(BK20141482)
【分類號】：R737.9;TP183
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本文編號：2037531