PDF(Pubmed)
Abstract：
UNASSIGNED: Whole Slide Image (WSI) analysis, driven by deep learning algorithms, has the potential to revolutionize tumor detection, classification, and treatment response prediction. However, challenges persist, such as limited model generalizability across various cancer types, the labor-intensive nature of patch-level annotation, and the necessity of integrating multi-magnification information to attain a comprehensive understanding of pathological patterns.
UNASSIGNED: In response to these challenges, we introduce MAMILNet, an innovative multi-scale attentional multi-instance learning framework for WSI analysis. The incorporation of attention mechanisms into MAMILNet contributes to its exceptional generalizability across diverse cancer types and prediction tasks. This model considers whole slides as \"bags\" and individual patches as \"instances.\" By adopting this approach, MAMILNet effectively eliminates the requirement for intricate patch-level labeling, significantly reducing the manual workload for pathologists. To enhance prediction accuracy, the model employs a multi-scale \"consultation\" strategy, facilitating the aggregation of test outcomes from various magnifications.
UNASSIGNED: Our assessment of MAMILNet encompasses 1171 cases encompassing a wide range of cancer types, showcasing its effectiveness in predicting complex tasks. Remarkably, MAMILNet achieved impressive results in distinct domains: for breast cancer tumor detection, the Area Under the Curve (AUC) was 0.8872, with an Accuracy of 0.8760. In the realm of lung cancer typing diagnosis, it achieved an AUC of 0.9551 and an Accuracy of 0.9095. Furthermore, in predicting drug therapy responses for ovarian cancer, MAMILNet achieved an AUC of 0.7358 and an Accuracy of 0.7341.
UNASSIGNED: The outcomes of this study underscore the potential of MAMILNet in driving the advancement of precision medicine and individualized treatment planning within the field of oncology. By effectively addressing challenges related to model generalization, annotation workload, and multi-magnification integration, MAMILNet shows promise in enhancing healthcare outcomes for cancer patients. The framework\'s success in accurately detecting breast tumors, diagnosing lung cancer types, and predicting ovarian cancer therapy responses highlights its significant contribution to the field and paves the way for improved patient care.
UNASSIGNED: In response to these challenges, we introduce MAMILNet, an innovative multi-scale attentional multi-instance learning framework for WSI analysis. The incorporation of attention mechanisms into MAMILNet contributes to its exceptional generalizability across diverse cancer types and prediction tasks. This model considers whole slides as \"bags\" and individual patches as \"instances.\" By adopting this approach, MAMILNet effectively eliminates the requirement for intricate patch-level labeling, significantly reducing the manual workload for pathologists. To enhance prediction accuracy, the model employs a multi-scale \"consultation\" strategy, facilitating the aggregation of test outcomes from various magnifications.
UNASSIGNED: Our assessment of MAMILNet encompasses 1171 cases encompassing a wide range of cancer types, showcasing its effectiveness in predicting complex tasks. Remarkably, MAMILNet achieved impressive results in distinct domains: for breast cancer tumor detection, the Area Under the Curve (AUC) was 0.8872, with an Accuracy of 0.8760. In the realm of lung cancer typing diagnosis, it achieved an AUC of 0.9551 and an Accuracy of 0.9095. Furthermore, in predicting drug therapy responses for ovarian cancer, MAMILNet achieved an AUC of 0.7358 and an Accuracy of 0.7341.
UNASSIGNED: The outcomes of this study underscore the potential of MAMILNet in driving the advancement of precision medicine and individualized treatment planning within the field of oncology. By effectively addressing challenges related to model generalization, annotation workload, and multi-magnification integration, MAMILNet shows promise in enhancing healthcare outcomes for cancer patients. The framework\'s success in accurately detecting breast tumors, diagnosing lung cancer types, and predicting ovarian cancer therapy responses highlights its significant contribution to the field and paves the way for improved patient care.
摘要：
■整个幻灯片图像（WSI）分析，由深度学习算法驱动，有可能彻底改变肿瘤检测，分类，和治疗反应预测。然而,挑战依然存在,例如在各种癌症类型中有限的模型泛化性，补丁级注释的劳动密集型性质，以及整合多倍放大信息以全面了解病理模式的必要性。
■为了应对这些挑战，我们介绍MAMILNet，用于WSI分析的创新多尺度注意多实例学习框架。将注意力机制纳入MAMILNet有助于其在不同癌症类型和预测任务中的非凡普遍性。此模型将整个幻灯片视为“bags”，将单个修补程序视为“实例”。“通过采用这种方法，MAMILNet有效地消除了复杂的补丁级别标签的要求，显著减少病理学家的人工工作量。为了提高预测准确性，该模型采用了多尺度的“咨询”策略，促进从各种放大倍数汇总测试结果。
■我们对MAMILNet的评估包括1171例，包括多种癌症类型，展示其在预测复杂任务方面的有效性。值得注意的是,MAMILNet在不同领域取得了令人印象深刻的成果：用于乳腺癌肿瘤检测，曲线下面积(AUC)为0.8872,准确度为0.8760。在肺癌分型诊断领域，它实现了0.9551的AUC和0.9095的准确性。此外，在预测卵巢癌的药物治疗反应方面，MAMILNet实现了0.7358的AUC和0.7341的准确性。
■这项研究的结果强调了MAMILNet在推动肿瘤领域内精准医学和个性化治疗计划的发展方面的潜力。通过有效解决与模型泛化相关的挑战，注释工作负载，和多放大倍数集成，MAMILNet在增强癌症患者的医疗保健结果方面显示出希望。该框架在准确检测乳腺肿瘤方面的成功，诊断肺癌类型，预测卵巢癌治疗反应突出了其对该领域的重大贡献，并为改善患者护理铺平了道路。
■为了应对这些挑战，我们介绍MAMILNet，用于WSI分析的创新多尺度注意多实例学习框架。将注意力机制纳入MAMILNet有助于其在不同癌症类型和预测任务中的非凡普遍性。此模型将整个幻灯片视为“bags”，将单个修补程序视为“实例”。“通过采用这种方法，MAMILNet有效地消除了复杂的补丁级别标签的要求，显著减少病理学家的人工工作量。为了提高预测准确性，该模型采用了多尺度的“咨询”策略，促进从各种放大倍数汇总测试结果。
■我们对MAMILNet的评估包括1171例，包括多种癌症类型，展示其在预测复杂任务方面的有效性。值得注意的是,MAMILNet在不同领域取得了令人印象深刻的成果：用于乳腺癌肿瘤检测，曲线下面积(AUC)为0.8872,准确度为0.8760。在肺癌分型诊断领域，它实现了0.9551的AUC和0.9095的准确性。此外，在预测卵巢癌的药物治疗反应方面，MAMILNet实现了0.7358的AUC和0.7341的准确性。
■这项研究的结果强调了MAMILNet在推动肿瘤领域内精准医学和个性化治疗计划的发展方面的潜力。通过有效解决与模型泛化相关的挑战，注释工作负载，和多放大倍数集成，MAMILNet在增强癌症患者的医疗保健结果方面显示出希望。该框架在准确检测乳腺肿瘤方面的成功，诊断肺癌类型，预测卵巢癌治疗反应突出了其对该领域的重大贡献，并为改善患者护理铺平了道路。
