Contrastive learning of global and local features for medical image segmentation with limited annotations
METADATA ONLY
Loading...
Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
A key requirement for the success of supervised deep learning is a large labeled dataset - a condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL) can help in this regard by providing a strategy to pre-train a neural network with unlabeled data, followed by fine-tuning for a downstream task with limited annotations. Contrastive learning, a particular variant of SSL, is a powerful technique for learning image-level representations. In this work, we propose strategies for extending the contrastive learning framework for segmentation of volumetric medical images in the semi-supervised setting with limited annotations, by leveraging domain-specific and problem-specific cues. Specifically, we propose (1) novel contrasting strategies that leverage structural similarity across volumetric medical images (domain-specific cue) and (2) a local version of the contrastive loss to learn distinctive representations of local regions that are useful for per-pixel segmentation (problem-specific cue). We carry out an extensive evaluation on three Magnetic Resonance Imaging (MRI) datasets. In the limited annotation setting, the proposed method yields substantial improvements compared to other self-supervision and semi-supervised learning techniques. When combined with a simple data augmentation technique, the proposed method reaches within 8\% of benchmark performance using only two labeled MRI volumes for training. The code is made public at https://github.com/krishnabits001/domain_specific_cl.
Permanent link
Publication status
published
Book title
Advances in Neural Information Processing Systems 33
Journal / series
Volume
Pages / Article No.
12546 - 12558
Publisher
Curran
Event
34th Annual Conference on Neural Information Processing Systems (NeurIPS 2020)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09579 - Konukoglu, Ender / Konukoglu, Ender
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
Funding
Related publications and datasets
Has part: