Self-Supervised Learning for Spinal MRIs

08/01/2017
by   Amir Jamaludin, et al.
0

A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network (CNN) is trained using two losses: (i) a contrastive loss on whether the scan is of the same person (i.e. longitudinal) or not, together with (ii) a classification loss on predicting the level of vertebral bodies. The performance of this pre-trained network is then assessed on a grading classification task. We experiment on a dataset of 1016 subjects, 423 possessing follow-up scans, with the end goal of learning the disc degeneration radiological gradings attached to the intervertebral discs. We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch.

READ FULL TEXT

page 4

page 5

research
01/14/2021

Self-Supervised Learning for Segmentation

Self-supervised learning is emerging as an effective substitute for tran...
research
04/17/2023

Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT

The lack of reliable biomarkers makes predicting the conversion from int...
research
06/16/2021

Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification

Traditional supervised learning with deep neural networks requires a tre...
research
05/25/2022

Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging

Training deep learning models on cardiac magnetic resonance imaging (CMR...
research
10/21/2019

Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning

Longitudinal imaging is capable of capturing the static anatomical struc...
research
07/14/2021

Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging

This paper explores the use of self-supervised deep learning in medical ...
research
05/29/2020

Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study

Pulmonary Embolism (PE) is a life-threatening disorder associated with h...

Please sign up or login with your details

Forgot password? Click here to reset