Abstract：
UNASSIGNED: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer.
UNASSIGNED: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches.
UNASSIGNED: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers.
UNASSIGNED: The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.
UNASSIGNED: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches.
UNASSIGNED: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers.
UNASSIGNED: The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.
摘要：
■乳腺癌的分子分类对于有效治疗至关重要。数字病理学的出现开创了一个新时代，在这个时代中，利用整个幻灯片图像的弱监督学习在开发深度学习模型中获得了突出地位，因为这种方法减轻了对大量手动注释的需求。采用弱监督学习对乳腺癌分子亚型进行分类。
我们的方法利用了两个全幻灯片图像数据集：一个由来自韩国大学Guro医院（KG）的乳腺癌病例组成，另一个来自癌症基因组图谱数据集（TCGA）。此外，我们使用基于注意力的热图可视化了推断的结果,并回顾了最专注的斑块的组织形态学特征.
■KG+TCGA训练的模型实现了0.749的接收器操作特征值下的区域。一个固有的挑战在于亚型之间的不平衡。此外,两个数据集之间的差异导致不同的分子亚型比例。为了缓解这种不平衡，我们合并了两个数据集，所得到的模型表现出改进的性能。注意的斑块与广泛认可的组织形态学特征密切相关。三阴性亚型高等级核的发生率高,肿瘤坏死,和肿瘤内浸润淋巴细胞。腔A亚型显示胶原纤维的高发生率。
■基于弱监督学习的人工智能（AI）模型显示出有希望的性能。对最专注的补丁的回顾提供了对AI模型预测的见解。人工智能模型可以成为宝贵的筛选工具，在实践中降低成本和工作量。
我们的方法利用了两个全幻灯片图像数据集：一个由来自韩国大学Guro医院（KG）的乳腺癌病例组成，另一个来自癌症基因组图谱数据集（TCGA）。此外，我们使用基于注意力的热图可视化了推断的结果,并回顾了最专注的斑块的组织形态学特征.
■KG+TCGA训练的模型实现了0.749的接收器操作特征值下的区域。一个固有的挑战在于亚型之间的不平衡。此外,两个数据集之间的差异导致不同的分子亚型比例。为了缓解这种不平衡，我们合并了两个数据集，所得到的模型表现出改进的性能。注意的斑块与广泛认可的组织形态学特征密切相关。三阴性亚型高等级核的发生率高,肿瘤坏死,和肿瘤内浸润淋巴细胞。腔A亚型显示胶原纤维的高发生率。
■基于弱监督学习的人工智能（AI）模型显示出有希望的性能。对最专注的补丁的回顾提供了对AI模型预测的见解。人工智能模型可以成为宝贵的筛选工具，在实践中降低成本和工作量。
