Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis
This work addresses breast cancer diagnosis by comparing existing methods on a specific dataset, representing an incremental evaluation study.
The study evaluated six state-of-the-art multi-instance multi-label learning methods on a subset of the digiPATH dataset of breast cancer histopathology images, finding that MIML-kNN achieved the best performance with 65.3% average precision while other methods also attained acceptable results.
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.