Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading

TL;DR

This paper introduces a self-supervised learning framework using a tailored convolutional autoencoder to classify prostate cancer grades from histopathological images, achieving promising accuracy with limited labeled data.

Contribution

It presents a novel SSL approach with a custom CAE for prostate cancer grading, addressing the challenge of scarce labeled data in histopathology.

Findings

Achieved 83% accuracy on validation set

Reached 76% accuracy and 77% F1-score on test set for G4

Demonstrated effectiveness of SSL in histopathological grading

Abstract

According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL) framework to classify prostate histopathological images when labeled images are scarce. In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct 128x128x3 patches of prostate cancer Whole Slide Images (WSIs) as a pretext task. The downstream task of the proposed SSL paradigm is the automatic grading of histopathological patches of prostate cancer. The presented framework reports promising results on the validation set, obtaining an overall accuracy of 83% and on the test set, achieving an overall accuracy value of 76% with F1-score of 77% in G4.