  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 technologies for different artificial intelligence
applications. 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 multi-target learning 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. We released a model
pre-trained with OSAMTL-DiNS for tumour segmentation in HE-stained
pre-treatment biopsy images in breast cancer, which has been successfully
applied as a pre-processing tool to extract tumour-associated stroma
compartment for predicting the pathological complete response to neoadjuvant
chemotherapy in breast cancer.
