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.

翻译：从含噪声标签中学习是许多实际场景中的重要问题。针对该问题，现有方法通常先对潜在噪声标签实例进行修正，再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取无噪声的真实标签，导致标签包含复杂噪声。这一现状引发了两个更具挑战性的问题：1）由于标签中存在复杂噪声，对潜在噪声标签实例进行修正的方法存在局限性；2）因难以收集无噪声的真实标签，验证/测试的合理评估策略尚不明确。针对问题1），我们提出单步溯因多目标学习（OSAMTL），该方法通过多目标学习过程将单步逻辑推理施加于机器学习，约束预测模型使其输出符合关于真实目标的先验知识。针对问题2），我们提出逻辑评估公式（LAF），通过估计模型预测与OSAMTL单步逻辑推理结果所描述逻辑事实之间的一致性，评估方法输出的逻辑合理性。基于MHWSIA中幽门螺杆菌（H. pylori）分割任务，我们证明OSAMTL能够使机器学习模型实现逻辑上更合理的预测，其在处理复杂噪声标签方面超越了多种最新方法。