2020-06-17

Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides    建议了预测评分模型，并比较不同模型的优劣。

Osareh, A. & Shadgar, B. in 2010 5th Int. Symp. Health Informat. Bioinformat. 114–120 (2010, IEEE). 

Dermatologist-level classification of skin cancer with deep neural networks. Nature 

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet （多中心、数量庞大，病种多，算法多）

Computerized nuclear morphometric features from H&E slide images are prognostic of recurrence and predictive of added benefit of adjuvant chemotherapy in early stage non-small cell lung cancer. Presented at the United States and Canadian Academy of Pathology’s 108th Annual Meeting. 

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 20, 938–947 (2019). 94. Couture, H. D. et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. npj Breast Cancer 4, 30 (2018). 

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection. PLOS ONE 13, e0196828 (2018). 

. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep

. Agendia. MammaPrint Test. Agendia.com https://www. agendia.com/our-tests/mammaprint/ (2019).