One-Step Abductive Multi-Target Learning with Diverse Noisy Samples

One-step abductive multi-target learning (OSAMTL) [1] was proposed to alleviate the situation where it is often difficult or even impossible for experts to manually achieve the accurate ground-truth labels, which leads to labels with complex noisy for a specific learning task. With a H. pylori segmentation task of medical histopathology whole slide images [1,2], OSAMTL has been shown to possess significant potentials in handling complex noisy labels, using logical rationality evaluations based on logical assessment formula (LAF) [1]. However, OSAMTL is not suitable for the situation of learning with diverse noisy samples. In this paper, we aim to address this issue. Firstly, we give definition of diverse noisy samples (DNS). Secondly, based on the given definition of DNS, we propose one-step abductive multi-target learning with DNS (OSAMTL-DNS). Finally, we provide analyses of OSAMTL-DNS compared with the original OSAMTL.