数字病理学中基于腐败仿真的深度学习模型鲁棒性评估与增强
Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology
AI总结：
本文针对数字病理全流程图像腐败问题提出OmniCE方法，可复现21种5级严重程度腐败并构建基准数据集，用于评估和增强DNN模型的鲁棒性与泛化能力。
摘要：
数字病理学中的深度学习为病理分析这一临床诊断的金标准带来了智能化和自动化方面的实质性增强。然而，从组织制备到切片成像的多个步骤会引入各种图像腐败，使得深度神经网络（DNN）模型难以在临床使用中获得稳定的诊断结果。为了评估并进一步增强模型的鲁棒性，我们分析了整个病理生命周期中全栈腐败的物理成因，并提出了一种全腐败仿真（OmniCE）方法，用于复现21种腐败类型，并以5级严重程度进行量化。随后，我们在切片级别和整张切片级别构建了三个OmniCE腐败基准数据集，并评估了流行DNN在分类和分割任务中的鲁棒性。此外，我们探索将OmniCE腐败数据集用作训练增强数据，并通过实验验证模型的泛化能力得到了显著增强。
英文摘要：
Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various image corruptions, making it difficult for deep neural network (DNN) models to achieve stable diagnostic results for clinical use. In order to assess and further enhance the robustness of the models, we analyze the physical causes of the full-stack corruptions throughout the pathological life-cycle and propose an Omni-Corruption Emulation (OmniCE) method to reproduce 21 types of corruptions quantified with 5-level severity. We then construct three OmniCE-corrupted benchmark datasets at both patch level and slide level and assess the robustness of popular DNNs in classification and segmentation tasks. Further, we explore to use the OmniCE-corrupted datasets as augmentation data for training and experiments to verify that the generalization ability of the models has been significantly enhanced.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
