Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced artificial intelligence technologies. One-step abductive multi-target learning (OSAMTL), an approach inspired by abductive learning, via simply combining machine learning and logical reasoning in a one-step balanced way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions.

翻译：近期研究表明，机器学习与逻辑推理的结合（包括数据驱动逻辑推理、知识驱动机器学习及溯因学习）在开发先进人工智能技术方面展现出显著效果。受溯因学习启发的一步溯因多目标学习（OSAMTL），通过以平衡方式简单结合机器学习和逻辑推理，已在医学组织病理学全切片图像分析（MHWSIA）中有效处理单一噪声样本的复杂标签。然而，OSAMTL并不适用于学习任务中提供多样化噪声样本（DiNS）的情境。本文在给出DiNS定义的基础上，提出面向DiNS的一步溯因多目标学习（OSAMTL-DiNS），将原始OSAMTL扩展至处理DiNS的复杂噪声标签。通过将OSAMTL-DiNS应用于MHWSIA中的乳腺癌肿瘤分割，我们证明该方法能使各类基于噪声标签学习的先进方法实现更合理的预测。