Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2503.14343v1
• Date: Tue, 18 Mar 2025 15:23:52 GMT
• Title: Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentation
• Authors: Yali Bi, Enyu Che, Yinan Chen, Yuanpeng He, Jingwei Qu,
• Abstract summary: We propose a Multi-Prototype-based Embedding Refinement method for semi-supervised medical image segmentation.<n>Specifically, we design a multi-prototype-based classification strategy, rethinking the segmentation from the perspective of structural relationships between voxel embeddings.<n>In the thorough evaluation on two popular benchmarks, our method achieves superior performance compared with state-of-the-art methods.
• Score: 1.6683976936678229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation aims to identify anatomical structures at the voxel-level. Segmentation accuracy relies on distinguishing voxel differences. Compared to advancements achieved in studies of the inter-class variance, the intra-class variance receives less attention. Moreover, traditional linear classifiers, limited by a single learnable weight per class, struggle to capture this finer distinction. To address the above challenges, we propose a Multi-Prototype-based Embedding Refinement method for semi-supervised medical image segmentation. Specifically, we design a multi-prototype-based classification strategy, rethinking the segmentation from the perspective of structural relationships between voxel embeddings. The intra-class variations are explored by clustering voxels along the distribution of multiple prototypes in each class. Next, we introduce a consistency constraint to alleviate the limitation of linear classifiers. This constraint integrates different classification granularities from a linear classifier and the proposed prototype-based classifier. In the thorough evaluation on two popular benchmarks, our method achieves superior performance compared with state-of-the-art methods. Code is available at https://github.com/Briley-byl123/MPER.

Related papers

• Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation [53.723234136550055]
  We term the new learning paradigm as Partially Supervised Unpaired Multi-Modal Learning (PSUMML)<n>We propose a novel Decomposed partial class adaptation with snapshot Ensembled Self-Training (DEST) framework for it.<n>Our framework consists of a compact segmentation network with modality specific normalization layers for learning with partially labeled unpaired multi-modal data.
  arXiv  Detail & Related papers  (2025-03-07T07:22:42Z)
• Convolutional autoencoder-based multimodal one-class classification [80.52334952912808]
  One-class classification refers to approaches of learning using data from a single class only. We propose a deep learning one-class classification method suitable for multimodal data.
  arXiv  Detail & Related papers  (2023-09-25T12:31:18Z)
• Efficient Subclass Segmentation in Medical Images [3.383033695275859]
  One feasible way to reduce the cost is to annotate with coarse-grained superclass labels while using limited fine-grained annotations as a complement. There is a lack of research on efficient learning of fine-grained subclasses in semantic segmentation tasks. Our approach achieves comparable accuracy to a model trained with full subclass annotations, with limited subclass annotations and sufficient superclass annotations.
  arXiv  Detail & Related papers  (2023-07-01T07:39:08Z)
• 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)
• Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization [20.4183741427867]
  Semi-supervised semantic segmentation requires the model to propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-pixel prediction task is the large intra-class variation. We propose a novel approach to regularize the distribution of within-class features to ease label propagation difficulty.
  arXiv  Detail & Related papers  (2022-10-10T01:38:01Z)
• A Similarity-based Framework for Classification Task [21.182406977328267]
  Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. We unite similarity-based learning and generalized linear models to achieve the best of both worlds.
  arXiv  Detail & Related papers  (2022-03-05T06:39:50Z)
• Exploring Category-correlated Feature for Few-shot Image Classification [27.13708881431794]
  We present a simple yet effective feature rectification method by exploring the category correlation between novel and base classes as the prior knowledge. The proposed approach consistently obtains considerable performance gains on three widely used benchmarks.
  arXiv  Detail & Related papers  (2021-12-14T08:25:24Z)
• 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)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches [67.51747235117]
  Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks. Recent works mainly tackle this problem by focusing on how to locate the most discriminative parts. We propose a novel framework for fine-grained visual classification to tackle these problems.
  arXiv  Detail & Related papers  (2020-03-08T19:27:30Z)

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.