Prototype Contrastive Consistency Learning for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2502.06650v1
• Date: Mon, 10 Feb 2025 16:40:26 GMT
• Title: Prototype Contrastive Consistency Learning for Semi-Supervised Medical Image Segmentation
• Authors: Shihuan He, Zhihui Lai, Ruxin Wang, Heng Kong,
• Abstract summary: Contrastive learning has proven to be effective for medical image segmentation in semi-supervised learning. We propose a prototype contrastive learning method called Prototype Contrastive Consistency (PCCS) for semi-supervised medical image segmentation. PCCS achieves better segmentation performance than the state-of-the-art methods.
• Score: 20.770556378026768
• License:
• Abstract: Medical image segmentation is a crucial task in medical image analysis, but it can be very challenging especially when there are less labeled data but with large unlabeled data. Contrastive learning has proven to be effective for medical image segmentation in semi-supervised learning by constructing contrastive samples from partial pixels. However, although previous contrastive learning methods can mine semantic information from partial pixels within images, they ignore the whole context information of unlabeled images, which is very important to precise segmentation. In order to solve this problem, we propose a novel prototype contrastive learning method called Prototype Contrastive Consistency Segmentation (PCCS) for semi-supervised medical image segmentation. The core idea is to enforce the prototypes of the same semantic class to be closer and push the prototypes in different semantic classes far away from each other. Specifically, we construct a signed distance map and an uncertainty map from unlabeled images. The signed distance map is used to construct prototypes for contrastive learning, and then we estimate the prototype uncertainty from the uncertainty map as trade-off among prototypes. In order to obtain better prototypes, based on the student-teacher architecture, a new mechanism named prototype updating prototype is designed to assist in updating the prototypes for contrastive learning. In addition, we propose an uncertainty-consistency loss to mine more reliable information from unlabeled data. Extensive experiments on medical image segmentation demonstrate that PCCS achieves better segmentation performance than the state-of-the-art methods. The code is available at https://github.com/comphsh/PCCS.

Related papers

• Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images [12.365801596593936]
  Medical image segmentation is one of the domains where sufficient annotated data is not available. We propose a prototype-based self-supervised one-way one-shot learning framework using pseudo-labels generated from superpixels. We show that the proposed simple but potent framework performs at par with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-12T15:38:51Z)
• Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation [0.0]
  We propose the Mixed Prototype Consistency Learning (MPCL) framework, which includes a Mean Teacher and an auxiliary network. The Mean Teacher generates prototypes for labeled and unlabeled data, while the auxiliary network produces additional prototypes for mixed data processed by CutMix. High-quality global prototypes for each class are formed by fusing two enhanced prototypes, optimizing the distribution of hidden embeddings used in consistency learning.
  arXiv  Detail & Related papers  (2024-04-16T16:51:12Z)
• Prototype Learning for Out-of-Distribution Polyp Segmentation [2.6179759969345002]
  Existing polyp segmentation models from colonoscopy images often fail to provide reliable segmentation results on datasets from different centers. Our model is designed to perform effectively on out-of-distribution (OOD) datasets from multiple centers. PrototypeLab offers a promising solution with a dice coefficient of $geq$ 90% and mIoU $geq$ 85% with a near real-time processing speed for polyp segmentation.
  arXiv  Detail & Related papers  (2023-08-07T16:30:24Z)
• 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)
• Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels [23.021720656733088]
  We propose a novel anomaly detection-inspired approach to few-shot medical image segmentation. We use a single foreground prototype to compute anomaly scores for all query pixels. The segmentation is then performed by thresholding these anomaly scores using a learned threshold.
  arXiv  Detail & Related papers  (2022-03-03T22:36:39Z)
• Diversified Multi-prototype Representation for Semi-supervised Segmentation [9.994508738317585]
  This work considers semi-supervised segmentation as a dense prediction problem based on prototype vector correlation. Two regularization strategies are applied to ensure that all prototype vectors are considered by the network. Experimental results on two benchmark medical segmentation datasets reveal our method's effectiveness in improving segmentation performance when few annotated images are available.
  arXiv  Detail & Related papers  (2021-11-16T17:33:58Z)
• Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation [55.339405417090084]
  We propose a dual prototypical contrastive learning approach tailored to the few-shot semantic segmentation (FSS) task. The main idea is to encourage the prototypes more discriminative by increasing inter-class distance while reducing intra-class distance in prototype feature space. We demonstrate that the proposed dual contrastive learning approach outperforms state-of-the-art FSS methods on PASCAL-5i and COCO-20i datasets.
  arXiv  Detail & Related papers  (2021-11-09T08:14:50Z)
• SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes [56.387647750094466]
  Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples. Most of advanced solutions exploit a metric learning framework that performs segmentation through matching each pixel to a learned foreground prototype. This framework suffers from biased classification due to incomplete construction of sample pairs with the foreground prototype only.
  arXiv  Detail & Related papers  (2021-04-19T11:21:47Z)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)
• Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation [58.96902899546075]
  One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimic the expected situation at testing time. We propose to leverage the multi-class label information during the episodic training. It will encourage the network to generate more semantically meaningful features for each category.
  arXiv  Detail & Related papers  (2021-02-22T12:07:35Z)
• Part-aware Prototype Network for Few-shot Semantic Segmentation [50.581647306020095]
  We propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes.
  arXiv  Detail & Related papers  (2020-07-13T11:03:09Z)

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.