論文筆記 | 基於深度學習的乳腺轉移瘤識別（Deep Learning for Identifying Metastatic Breast Cancer）
論文閱讀筆記：原文鏈接 摘要 生物醫學圖像國際研討會（International Symposium on Biomedical Imaging，ISBI）舉辦了一個在前哨淋巴節點全幻燈圖中自動檢測轉移性乳腺癌的挑戰賽。 該隊全幻燈圖分類準確AUC準確率爲0.925，癌細胞定位得分爲0.7051。病理學家的分類AUC準確率爲0.966，癌細胞定位得分爲0.733。 將病理學家診斷和深度學習系統預測
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相關文章
- 1. 論文閱讀《Deep Learning for Identifying Metastatic Breast Cancer》
- 2. 論文筆記 / Deep features for breast cancer histopathological image classification
- 3. 論文閱讀《Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images》
- 4. Grand Challenge on Breast Cancer Histology Images
- 5. Sequential modeling of deep features for breast cancer histopathological image classification 論文閱讀
- 6. 論文筆記《Adversarial Networks for Prostate Cancer Detection》
- 7. 論文筆記：End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design
- 8. Multitask Classification of Breast Cancer Pathological Images Using SE-DenseNet
- 9. 論文筆記（四）《Adversarial Networks for the Detection of Aggressive Prostate Cancer》
- 10. 【深度學習論文學習筆記】Deep Residual Learning for Image Recognition【2015】