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：最近的研究表明,机器学习和逻辑推理相结合,包括数据驱动逻辑推理、知识驱动机器学习和绑架性学习,在发明先进的人工智能技术方面是有效的。一步制式多目标学习(OSAMTL),这是由绑架性学习所启发的一种方法,它只是将机器学习和逻辑推理以一步平衡的方式结合起来,它也显示了它在处理医学病理学全幻灯片图象分析中单一噪音样本的复杂噪音标签方面的有效性。然而,OSAMTL不适合提供各种噪音样本(DINS)进行学习工作的情况。在本文中,我们提出了DINS(OSAMTL-DINS)的定义,我们建议与DINS(OSAMTL-DINS)一起进行一步制式的多目标学习,以扩大最初的OSAMTL(OSMTL)处理DINS复杂的噪音标签。在MWSIA中应用OSAMTL-DINS到乳腺癌肿瘤分解,我们表明,OSAMTL-DNS能够使各种从噪音标签中学习的状态方法得以实现更理性的预测。