Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2203.01324v1
• Date: Wed, 2 Mar 2022 08:38:09 GMT
• Title: Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
• Authors: Yicheng Wu, Zhonghua Wu, Qianyi Wu, Zongyuan Ge, and Jianfei Cai
• Abstract summary: We propose the SS-Net for semi-supervised medical image segmentation tasks. pixel-level smoothness forces the model to generate invariant results under adversarial perturbations. The inter-class separation constrains individual class features should approach their corresponding high-quality prototypes.
• Score: 39.068698033394064
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often limited and there are many blurred pixels near the adhesive edges or low-contrast regions. To address the issues, we advocate to firstly constrain the consistency of samples with and without strong perturbations to apply sufficient smoothness regularization and further encourage the class-level separation to exploit the unlabeled ambiguous pixels for the model training. Particularly, in this paper, we propose the SS-Net for semi-supervised medical image segmentation tasks, via exploring the pixel-level Smoothness and inter-class Separation at the same time. The pixel-level smoothness forces the model to generate invariant results under adversarial perturbations. Meanwhile, the inter-class separation constrains individual class features should approach their corresponding high-quality prototypes, in order to make each class distribution compact and separate different classes. We evaluated our SS-Net against five recent methods on the public LA and ACDC datasets. The experimental results under two semi-supervised settings demonstrate the superiority of our proposed SS-Net, achieving new state-of-the-art (SOTA) performance on both datasets. The codes will be released.

Related papers

• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation [26.933651788004475]
  We propose a novel semi-supervised segmentation method named Rectified Contrastive Pseudo Supervision (RCPS) RCPS combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-13T12:03:58Z)
• IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation [3.6748639131154315]
  We extend the concept of metric learning to the segmentation task. We propose a simple convolutional projection head for obtaining dense pixel-level features. A bidirectional regularization mechanism involving two-stream regularization training is devised for the downstream task.
  arXiv  Detail & Related papers  (2022-10-26T23:11:02Z)
• 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)
• Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation [119.009033745244]
  This paper presents a Self-supervised Low-Rank Network ( SLRNet) for single-stage weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS) SLRNet uses cross-view self-supervision, that is, it simultaneously predicts several attentive LR representations from different views of an image to learn precise pseudo-labels. Experiments on the Pascal VOC 2012, COCO, and L2ID datasets demonstrate that our SLRNet outperforms both state-of-the-art WSSS and SSSS methods with a variety of different settings.
  arXiv  Detail & Related papers  (2022-03-19T09:19:55Z)
• An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation [0.0]
  The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. We introduce a novel regularization strategy involving computation-based mixing for semi-supervised medical image segmentation.
  arXiv  Detail & Related papers  (2022-02-01T16:21:14Z)
• Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical Image Segmentation [68.9233942579956]
  We propose a novel mutual consistency network (MC-Net+) to exploit the unlabeled hard regions for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions. We compare the segmentation results of the MC-Net+ with five state-of-the-art semi-supervised approaches on three public medical datasets.
  arXiv  Detail & Related papers  (2021-09-21T04:47:42Z)
• 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)
• Uncertainty guided semi-supervised segmentation of retinal layers in OCT images [4.046207281399144]
  We propose a novel uncertainty-guided semi-supervised learning based on a student-teacher approach for training the segmentation network. The proposed framework is a key contribution and applicable for biomedical image segmentation across various imaging modalities.
  arXiv  Detail & Related papers  (2021-03-02T23:14:25Z)
• Pairwise Relation Learning for Semi-supervised Gland Segmentation [90.45303394358493]
  We propose a pairwise relation-based semi-supervised (PRS2) model for gland segmentation on histology images. This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net) We evaluate our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset.
  arXiv  Detail & Related papers  (2020-08-06T15:02:38Z)

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.