Contrastive learning of global and local features for medical image segmentation with limited annotations

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. Selfsupervised 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, corresponding to only 4% (for ACDC) of the training data used to train the benchmark.Preprint. Under review.

Contrastive Learning for Neural Topic Model

“…There are various efforts studying contrastive method to learn meaningful representations. For visual information, contrastive framework is applied for tasks such as image classification [29,30], object detection [31][32][33], image segmentaion [34][35][36], etc. Other applications different from image include adversarial training [37][38][39], graph [40][41][42][43], and sequence modeling [44][45][46].…”

“…The full proof of (4) can be found in the Appendix. Previous works [39,35,40,29,20,50] consider the positive and negative sample equally likely as setting β = 1. In this paper, we leverage different values of β to guide the model concentration on the sample which is distinct from the input.…”

Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI

“…In the medical domain, popular techniques to leverage unlabelled samples include transfer learning from a distant or related task, and self-training with automatically generated labels [7]. Other techniques to leverage unlabelled samples include contrastive learning [8]- [10] and self-supervised representation learning [11]. These techniques either pre-train without labels, or directly use model predictions as true labels.…”

Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts

“…Nowadays, contrastive representation learning has been widely applied and outstandingly successful in medical image analysis [51,33,6]. The goal of contrastive learning is to learn invariant representations via contrasting medical image pairs, which can be regarded as an implicit way to preserve maximal information.…”