Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image
Segmentation
- URL: http://arxiv.org/abs/2306.14293v1
- Date: Sun, 25 Jun 2023 16:55:32 GMT
- Title: Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image
Segmentation
- Authors: Qianying Liu, Xiao Gu, Paul Henderson, Fani Deligianni
- Abstract summary: We develop a novel Multi-Scale Cross Supervised Contrastive Learning framework to segment structures in medical images.
Our approach contrasts multi-scale features based on ground-truth and cross-predicted labels, in order to extract robust feature representations.
It outperforms state-of-the-art semi-supervised methods by more than 3.0% in Dice.
- Score: 14.536384387956527
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semi-supervised learning has demonstrated great potential in medical image
segmentation by utilizing knowledge from unlabeled data. However, most existing
approaches do not explicitly capture high-level semantic relations between
distant regions, which limits their performance. In this paper, we focus on
representation learning for semi-supervised learning, by developing a novel
Multi-Scale Cross Supervised Contrastive Learning (MCSC) framework, to segment
structures in medical images. We jointly train CNN and Transformer models,
regularising their features to be semantically consistent across different
scales. Our approach contrasts multi-scale features based on ground-truth and
cross-predicted labels, in order to extract robust feature representations that
reflect intra- and inter-slice relationships across the whole dataset. To
tackle class imbalance, we take into account the prevalence of each class to
guide contrastive learning and ensure that features adequately capture
infrequent classes. Extensive experiments on two multi-structure medical
segmentation datasets demonstrate the effectiveness of MCSC. It not only
outperforms state-of-the-art semi-supervised methods by more than 3.0% in Dice,
but also greatly reduces the performance gap with fully supervised methods.
Related papers
- Rethinking Semi-Supervised Medical Image Segmentation: A
Variance-Reduction Perspective [51.70661197256033]
We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation.
We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks.
We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
arXiv Detail & Related papers (2023-02-03T13:50:25Z) - Consistency-Based Semi-supervised Evidential Active Learning for
Diagnostic Radiograph Classification [2.3545156585418328]
We introduce a novel Consistency-based Semi-supervised Evidential Active Learning framework (CSEAL)
We leverage predictive uncertainty based on theories of evidence and subjective logic to develop an end-to-end integrated approach.
Our approach can substantially improve accuracy on rarer abnormalities with fewer labelled samples.
arXiv Detail & Related papers (2022-09-05T09:28:31Z) - PCA: Semi-supervised Segmentation with Patch Confidence Adversarial
Training [52.895952593202054]
We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation.
PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state.
Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
arXiv Detail & Related papers (2022-07-24T07:45:47Z) - MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for
Semi-supervised Cardiac Image Segmentation [18.275478722238123]
We propose a novel multi-scale multi-view global-local contrastive learning framework for medical image segmentation.
Experiments on the MM-WHS dataset demonstrate the effectiveness of MMGL framework on semi-supervised cardiac image segmentation.
arXiv Detail & Related papers (2022-07-05T08:24:46Z) - Voxel-wise Adversarial Semi-supervised Learning for Medical Image
Segmentation [4.489713477369384]
We introduce a novel adversarial learning-based semi-supervised segmentation method for medical image segmentation.
Our method embeds both local and global features from multiple hidden layers and learns context relations between multiple classes.
Our method outperforms current best-performing state-of-the-art semi-supervised learning approaches on the image segmentation of the left atrium (single class) and multiorgan datasets (multiclass)
arXiv Detail & Related papers (2022-05-14T06:57:19Z) - Cross-level Contrastive Learning and Consistency Constraint for
Semi-supervised Medical Image Segmentation [46.678279106837294]
We propose a cross-level constrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image segmentation.
With the help of the cross-level contrastive learning and consistency constraint, the unlabelled data can be effectively explored to improve segmentation performance.
arXiv Detail & Related papers (2022-02-08T15:12:11Z) - Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
Several multimodal representation learning approaches have been proposed that jointly represent image and text.
These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining.
We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
arXiv Detail & Related papers (2021-09-24T07:20:13Z) - Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation [16.517086214275654]
We present a novel semi-supervised 2D medical segmentation solution that applies Contrastive Learning (CL) on image patches, instead of full images.
These patches are meaningfully constructed using the semantic information of different classes obtained via pseudo labeling.
We also propose a novel consistency regularization scheme, which works in synergy with contrastive learning.
arXiv Detail & Related papers (2021-06-12T15:43:24Z) - Few-shot Medical Image Segmentation using a Global Correlation Network
with Discriminative Embedding [60.89561661441736]
We propose a novel method for few-shot medical image segmentation.
We construct our few-shot image segmentor using a deep convolutional network trained episodically.
We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
arXiv Detail & Related papers (2020-12-10T04:01:07Z) - Towards Robust Partially Supervised Multi-Structure Medical Image
Segmentation on Small-Scale Data [123.03252888189546]
We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity.
Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels.
Our research suggests a new research direction in label-efficient deep learning with partial supervision.
arXiv Detail & Related papers (2020-11-28T16:31:00Z) - Unpaired Multi-modal Segmentation via Knowledge Distillation [77.39798870702174]
We propose a novel learning scheme for unpaired cross-modality image segmentation.
In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI.
We have extensively validated our approach on two multi-class segmentation problems.
arXiv Detail & Related papers (2020-01-06T20:03:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.