One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer

TL;DR

This paper introduces OSAMTL-DiNS, an extension of one-step abductive multi-target learning, designed to handle diverse noisy samples in medical image analysis, improving tumour segmentation accuracy in breast cancer histopathology.

Contribution

The paper proposes OSAMTL-DiNS, a novel method that effectively manages complex noisy labels from diverse sources, enhancing machine learning performance in medical image segmentation tasks.

Findings

OSAMTL-DiNS improves prediction rationality in noisy label scenarios.

The pre-trained model aids in tumour segmentation and response prediction in breast cancer.

Method outperforms existing approaches in handling diverse noisy data.

Abstract

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.