常规提供大肠癌患者的组织染色玫瑰紫-伊红染色活检切片。这些切片通常未用于确定用于患者分层和治疗选择的客观生物标志物。标准生物标志物通常与昂贵且缓慢的基因测试相关。然而，最近的研究表明，可以使用卷积神经网络（CNN）从这些图像中提取相关生物标志物。基于CNN的生物标志物在预测大肠癌患者预后方面与黄金标准相当。提取CNN生物标志物快速、自动且成本最低。基于CNN的生物标志物依赖于CNN识别显微镜全切片图像中不同组织类型的能力。这些生物标志物的质量（被称为“深层间质”）取决于CNN在分解所有相关组织类别方面的准确性。提高组织分解准确性对于提高CNN生物标志物的预测潜力至关重要。在本研究中，我们实施了一种新颖的训练策略来改进一个已建立的CNN模型，该模型超过了所有先前的解决方案。我们在外部测试集中获得了95.6%的平均准确度，在内部测试集中获得了99.5%的准确度。我们的方法减少了生物标志物相关类别（如淋巴细胞）中的错误，并且是第一个包括可解释性方法的方法。这些方法被用于更好地理解我们模型的局限性和能力。© 2023 Springer Nature Limited.

Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests. However, recent work has shown that relevant biomarkers can be extracted from these images using convolutional neural networks (CNNs). The CNN-based biomarkers predicted colorectal cancer patient outcomes comparably to gold standards. Extracting CNN-biomarkers is fast, automatic, and of minimal cost. CNN-based biomarkers rely on the ability of CNNs to recognize distinct tissue types from microscope whole slide images. The quality of these biomarkers (coined 'Deep Stroma') depends on the accuracy of CNNs in decomposing all relevant tissue classes. Improving tissue decomposition accuracy is essential for improving the prognostic potential of CNN-biomarkers. In this study, we implemented a novel training strategy to refine an established CNN model, which then surpassed all previous solutions . We obtained a 95.6% average accuracy in the external test set and 99.5% in the internal test set. Our approach reduced errors in biomarker-relevant classes, such as Lymphocytes, and was the first to include interpretability methods. These methods were used to better apprehend our model's limitations and capabilities.© 2023. Springer Nature Limited.