Learning from noisy labels is an important concern because of the lack of accurate ground-truth labels in plenty of real-world scenarios. In practice, 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 even impossible for experts to manually achieve the noisy-free ground-truth labels which leads to labels with heavy 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 heavy 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. In this paper, we focus on alleviating these two problems. For the problem 1), we present a one-step abductive multi-target learning framework (OSAMTLF) that imposes a one-step logical reasoning upon machine learning via a multi-target learning procedure to abduct the predictions of the learning model to be subject to our prior knowledge. 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 OSAMTLF. Applying OSAMTLF and LAF to the Helicobacter pylori (H. pylori) segmentation task in MHWSIA, we show that OSAMTLF is able to abduct the machine learning model achieving logically more rational predictions, which is beyond the capability of various state-of-the-art approaches for learning from noisy labels.

中文翻译：

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通过一步式归纳多目标学习处理嘈杂的标签

从嘈杂的标签中学习是一个重要的问题，因为在许多实际场景中缺少准确的真实标签。在实践中，针对此问题的各种方法首先进行与可能带有噪声标记的实例相对应的校正，然后使用进行的校正信息更新预测模型。但是，在特定领域，例如医学组织病理学全玻片图像分析（MHWSIA），专家通常很难甚至根本无法手动获得无噪音的地面标签，从而导致标签噪声大。这种情况带来了两个更困难的问题：1）由于标签中存在的大量噪声，相应于可能带有噪声标签的实例进行校正的方法的方法存在局限性；2）由于收集无噪音地面真相标签的难度很大，因此尚不清楚用于验证/测试的适当评估策略。在本文中，我们着重于缓解这两个问题。对于问题1），我们提出了一种一步式归纳多目标学习框架（OSAMTLF），该框架通过多目标学习过程对机器学习施加了一步逻辑推理，以绑架要学习的学习模型的预测根据我们的先验知识。对于问题2），我们提出了一种逻辑评估公式（LAF），该逻辑评估公式通过估算学习模型的预测与单步结果所描述的逻辑事实之间的一致性来评估方法输出的逻辑合理性OSAMTLF的逻辑推理。