通过一步溯因多目标学习处理噪声标签及其在幽门螺杆菌分割中的应用
Handling Noisy Labels via One-Step Abductive Multi-Target Learning and Its Application to Helicobacter Pylori Segmentation
AI总结：
本文针对医学全切片图像分析中复杂噪声标签问题，提出一步溯因多目标学习（OSAMTL）约束模型预测服从先验知识，并设计逻辑评估公式（LAF）评估输出合理性，在幽门螺杆菌分割任务上取得优于现有方法的逻辑更合理的预测。
摘要：
从噪声标签中学习是大量现实场景中的一个重要问题。针对这一问题，各种方法首先对可能带有噪声标签的实例进行修正，然后利用修正信息更新预测模型。然而，在特定领域（如医学组织病理学全切片图像分析，MHWSIA）中，专家往往难以或无法人工获得无噪声的真实标签，这导致标签中存在复杂噪声。这种情况带来了两个更困难的问题：1）由于标签中存在的复杂噪声，对可能带有噪声标签的实例进行修正的方法论存在局限性；2）由于收集无噪声真实标签的难度极大，验证/测试的适当评估策略尚不明确。针对问题1），我们提出了一步溯因多目标学习（OSAMTL），该方法通过多目标学习过程在机器学习之上施加一步逻辑推理，以约束学习模型的预测服从我们关于真实目标的先验知识。针对问题2），我们提出了一种逻辑评估公式（LAF），该公式通过估计学习模型的预测与由OSAMTL的一步逻辑推理结果所叙述的逻辑事实之间的一致性，来评估某一方法输出的逻辑合理性。基于MHWSIA中的幽门螺杆菌（H. pylori）分割任务，我们表明OSAMTL使机器学习模型能够实现逻辑上更合理的预测，超越了各种处理复杂噪声标签的最新方法。
英文摘要：
Learning from noisy labels is an important concern in plenty of real-world scenarios. Various approaches for this concern first make corrections corresponding to potentially noisy-labeled instances, and then update predictive model with information of the made corrections. However, in specific areas, such as medical histopathology whole slide image analysis (MHWSIA), it is often difficult or impossible for experts to manually achieve the noisy-free ground-truth labels which leads to labels with complex noise. This situation raises two more difficult problems: 1) the methodology of approaches making corrections corresponding to potentially noisy-labeled instances has limitations due to the complex noise existing in labels; and 2) the appropriate evaluation strategy for validation/testing is unclear because of the great difficulty in collecting the noisy-free ground-truth labels. For the problem 1), we present one-step abductive multi-target learning (OSAMTL) that imposes a one-step logical reasoning upon machine learning via a multi-target learning procedure to constrain the predictions of the learning model to be subject to our prior knowledge about the true target. For the problem 2), we propose a logical assessment formula (LAF) that evaluates the logical rationality of the outputs of an approach by estimating the consistencies between the predictions of the learning model and the logical facts narrated from the results of the one-step logical reasoning of OSAMTL. Based on the Helicobacter pylori (H. pylori) segmentation task in MHWSIA, we show that OSAMTL enables the machine learning model achieving logically more rational predictions, which is beyond various state-of-the-art approaches in handling complex noisy labels.
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。
