上下文在数字病理学中重要吗？
Does context matter in digital pathology?
AI总结：
本研究探讨深度学习模型在数字病理诊断中是否利用周围组织作为上下文，发现上下文信息对预测性能至关重要，且部分上下文可能导致不稳定预测。
摘要：
人工智能在医疗保健领域的发展至关重要。模型有时甚至能达到优于人类专家的性能，然而，它们可能基于虚假特征进行推理。这对专家来说是不可接受的，因为期望模型遵循领域专业知识捕捉数据中的有效模式。在这项工作中，我们分析了用于视觉的深度学习（DL）模型是否遵循组织病理学家的实践，即在诊断病变的一部分时，它们也会考虑作为上下文的周围组织。结果表明，当上下文信息量有限时，深度学习模型的性能显著下降，因此上下文信息在预测时是有价值的。此外，我们表明，模型有时表现不稳定，因为对于某些图像，它们会根据上下文的大小多次改变预测。这可能表明部分上下文信息可能具有误导性。
英文摘要：
The development of Artificial Intelligence for healthcare is of great importance. Models can sometimes achieve even superior performance to human experts, however, they can reason based on spurious features. This is not acceptable to the experts as it is expected that the models catch the valid patterns in the data following domain expertise. In the work, we analyse whether Deep Learning (DL) models for vision follow the histopathologists' practice so that when diagnosing a part of a lesion, they take into account also the surrounding tissues which serve as context. It turns out that the performance of DL models significantly decreases when the amount of contextual information is limited, therefore contextual information is valuable at prediction time. Moreover, we show that the models sometimes behave in an unstable way as for some images, they change the predictions many times depending on the size of the context. It may suggest that partial contextual information can be misleading.
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。
