Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound

by   Paul F. R. Wilson, et al.

Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of supervised learning methods. On the other hand, unlabeled prostate ultrasound data are more abundant. In this work, we successfully apply self-supervised representation learning to micro-ultrasound data. Using ultrasound data from 1028 biopsy cores of 391 subjects obtained in two clinical centres, we demonstrate that feature representations learnt with this method can be used to classify cancer from non-cancer tissue, obtaining an AUROC score of 91 this is the first successful end-to-end self-supervised learning approach for prostate cancer detection using ultrasound data. Our method outperforms baseline supervised learning approaches, generalizes well between different data centers, and scale well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data.


page 1

page 3

page 4

page 6


TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-Supervision

A large body of previous machine learning methods for ultrasound-based p...

Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound

MOTIVATION: Detection of prostate cancer during transrectal ultrasound-g...

Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images

This work presents a novel self-supervised pre-training method to learn ...

Recent Advancements in Self-Supervised Paradigms for Visual Feature Representation

We witnessed a massive growth in the supervised learning paradigm in the...

VertMatch: A Semi-supervised Framework for Vertebral Structure Detection in 3D Ultrasound Volume

Three-dimensional (3D) ultrasound imaging technique has been applied for...

Building Damage Mapping with Self-PositiveUnlabeled Learning

Humanitarian organizations must have fast and reliable data to respond t...

Self-supervised learning unveils morphological clusters behind lung cancer types and prognosis

Histopathological images of tumors contain abundant information about ho...

Please sign up or login with your details

Forgot password? Click here to reset