Grand Challenge on Breast Cancer Histology Images
目前該挑戰最好的成績是87%的精度，都使用了卷積神經網絡 1.介紹 通常在檢測出異常的時候需要進行乳房組織活性檢測，採集的組織樣本用蘇木精和伊紅染色（H&E），以便區分細胞核和實質，並通過光學顯微鏡觀察。這些樣本也可以在千兆分辨率下掃描，稱爲整體幻燈片圖像（WSI），用於後向數字處理。病理學家通過分析乳腺組織的組織學特性，在組織的微觀部分尋找癌症的跡象。由於乳腺樣本正常、良性和惡性（原位或
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相關文章
- 1. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
- 2. Multitask Classification of Breast Cancer Pathological Images Using SE-DenseNet
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- 9. A Unified Framework for Tumor ProliferationScore Prediction in Breast Histopathology
- 10. 論文筆記 / Deep features for breast cancer histopathological image classification