A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images

TL;DR

This paper introduces a self-learning deep clustering framework for grading muscle-invasive bladder cancer from histopathological images, achieving high accuracy without requiring labeled data and aligning with clinical pattern recognition.

Contribution

The novel DCEAC model enables unsupervised grading of bladder cancer severity, surpassing previous methods and learning clinically relevant patterns without prior annotations.

Findings

Achieves 90.34% accuracy in multi-class bladder cancer grading.

Outperforms previous clustering methods by 2-3%.

Learns clinically relevant features without labeled data.

Abstract

Recently, bladder cancer has been significantly increased in terms of incidence and mortality. Currently, two subtypes are known based on tumour growth: non-muscle invasive (NMIBC) and muscle-invasive bladder cancer (MIBC). In this work, we focus on the MIBC subtype because it is of the worst prognosis and can spread to adjacent organs. We present a self-learning framework to grade bladder cancer from histological images stained via immunohistochemical techniques. Specifically, we propose a novel Deep Convolutional Embedded Attention Clustering (DCEAC) which allows classifying histological patches into different severity levels of the disease, according to the patterns established in the literature. The proposed DCEAC model follows a two-step fully unsupervised learning methodology to discern between non-tumour, mild and infiltrative patterns from high-resolution samples of 512x512 pixels. Our system outperforms previous clustering-based methods by including a convolutional attention module, which allows refining the features of the latent space before the classification stage. The proposed network exceeds state-of-the-art approaches by 2-3% across different metrics, achieving a final average accuracy of 0.9034 in a multi-class scenario. Furthermore, the reported class activation maps evidence that our model is able to learn by itself the same patterns that clinicians consider relevant, without incurring prior annotation steps. This fact supposes a breakthrough in muscle-invasive bladder cancer grading which bridges the gap with respect to train the model on labelled data.