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  Indian J Med Microbiol
 

Figure 1: Deep convolutional neural networks were trained on images of hematoxylin and eosin-stained tumor tissue microarray spots from a nationwide breast cancer series (FinProg) to predict risk scores of breast cancer-specific survival. The training was performed using a transfer learning approach with ImageNet pretrained weights. The multitask approach combined outcome-supervised and biomarker-supervised feature learning. At the test phase, the networks generate a risk score for each patient in the test sets which consisted of FinProg test set patients and patients from the FinHer series. Additionally, conventional tissue entities in the tissue microarray spot images in the FinProg test set were assessed by a pathologist, i.e., mitoses, nuclear pleomorphism, tubules, tissue necrosis and tumor-infiltrating lymphocytes. Finally, a survival analysis on expert-derived and deep learning-based features was performed using Cox Proportional Hazards method.

Figure 1: Deep convolutional neural networks were trained on images of hematoxylin and eosin-stained tumor tissue microarray spots from a nationwide breast cancer series  (FinProg) to predict risk scores of breast cancer-specific survival. The training was performed using a transfer learning approach with ImageNet pretrained weights. The multitask approach combined outcome-supervised and biomarker-supervised feature learning. At the test phase, the networks generate a risk score for each patient in the test sets which consisted of FinProg test set patients and patients from the FinHer series. Additionally, conventional tissue entities in the tissue microarray spot images in the FinProg test set were assessed by a pathologist, i.e., mitoses, nuclear pleomorphism, tubules, tissue necrosis and tumor-infiltrating lymphocytes. Finally, a survival analysis on expert-derived and deep learning-based features was performed using Cox Proportional Hazards method.