Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings

TL;DR

This paper introduces a similarity-based multi-scale embedding approach for histopathological image classification that is robust to magnification variations and class imbalance, significantly improving accuracy on benchmark datasets.

Contribution

The paper proposes a novel SMSE method utilizing pair and triplet losses to learn magnification-independent embeddings, addressing class imbalance with a reinforced focal loss.

Findings

Improves classification accuracy for breast and liver cancer images.

Achieves 5-18% better performance on the BreakHis benchmark.

Demonstrates robustness to magnification variations.

Abstract

The classification of histopathological images is of great value in both cancer diagnosis and pathological studies. However, multiple reasons, such as variations caused by magnification factors and class imbalance, make it a challenging task where conventional methods that learn from image-label datasets perform unsatisfactorily in many cases. We observe that tumours of the same class often share common morphological patterns. To exploit this fact, we propose an approach that learns similarity-based multi-scale embeddings (SMSE) for magnification-independent histopathological image classification. In particular, a pair loss and a triplet loss are leveraged to learn similarity-based embeddings from image pairs or image triplets. The learned embeddings provide accurate measurements of similarities between images, which are regarded as a more effective form of representation for histopathological morphology than normal image features. Furthermore, in order to ensure the generated models are magnification-independent, images acquired at different magnification factors are simultaneously fed to networks during training for learning multi-scale embeddings. In addition to the SMSE, to eliminate the impact of class imbalance, instead of using the hard sample mining strategy that intuitively discards some easy samples, we introduce a new reinforced focal loss to simultaneously punish hard misclassified samples while suppressing easy well-classified samples. Experimental results show that the SMSE improves the performance for histopathological image classification tasks for both breast and liver cancers by a large margin compared to previous methods. In particular, the SMSE achieves the best performance on the BreakHis benchmark with an improvement ranging from 5% to 18% compared to previous methods using traditional features.