Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis

TL;DR

This paper evaluates six state-of-the-art multi-instance multi-label learning methods on breast cancer histopathology images, finding MIML-kNN achieves the highest average precision of 65.3%, demonstrating the potential of MIML approaches in medical diagnosis.

Contribution

The study compares multiple MIML methods on a breast cancer dataset, highlighting the effectiveness of MIML-kNN for histopathology image classification.

Findings

MIML-kNN achieved 65.3% average precision.

Most methods attained acceptable results.

Performance comparison provides insights for medical diagnosis applications.

Abstract

Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-of-the-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most of other methods attain acceptable results as well.