Semi-Supervised Medical Image Segmentation via Dual Networks

• URL: http://arxiv.org/abs/2505.17690v1
• Date: Fri, 23 May 2025 09:59:26 GMT
• Title: Semi-Supervised Medical Image Segmentation via Dual Networks
• Authors: Yunyao Lu, Yihang Wu, Reem Kateb, Ahmad Chaddad,
• Abstract summary: We propose an innovative semi-supervised 3D medical image segmentation method to reduce the dependency on large, expert-labeled datasets.<n>We introduce a dual-network architecture to address the limitations of existing methods in using contextual information.<n> Experiments on clinical magnetic resonance imaging demonstrate that our approach outperforms state-of-the-art techniques.
• Score: 1.904929457002693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional supervised medical image segmentation models require large amounts of labeled data for training; however, obtaining such large-scale labeled datasets in the real world is extremely challenging. Recent semi-supervised segmentation models also suffer from noisy pseudo-label issue and limited supervision in feature space. To solve these challenges, we propose an innovative semi-supervised 3D medical image segmentation method to reduce the dependency on large, expert-labeled datasets. Furthermore, we introduce a dual-network architecture to address the limitations of existing methods in using contextual information and generating reliable pseudo-labels. In addition, a self-supervised contrastive learning strategy is used to enhance the representation of the network and reduce prediction uncertainty by distinguishing between reliable and unreliable predictions. Experiments on clinical magnetic resonance imaging demonstrate that our approach outperforms state-of-the-art techniques. Our code is available at https://github.com/AIPMLab/Semi-supervised-Segmentation.

Related papers

• RefineSeg: Dual Coarse-to-Fine Learning for Medical Image Segmentation [2.608565452856053]
  High-quality pixel-level annotations of medical images are essential for supervised segmentation tasks.<n>We propose a novel coarse-to-fine segmentation framework that relies entirely on coarse-level annotations.
  arXiv  Detail & Related papers  (2025-08-04T19:14:30Z)
• Robust Noisy Pseudo-label Learning for Semi-supervised Medical Image Segmentation Using Diffusion Model [5.158113225132093]
  Semi-supervised medical image segmentation aims to leverage limited annotated data alongside abundant unlabeled data to achieve accurate segmentation.<n>Existing methods often struggle to structure semantic distributions in the latent space due to noise introduced by pseudo-labels.<n>Our method introduces a constraint into the latent structure of semantic labels during the denoising diffusion process by enforcing prototype-based contrastive consistency.
  arXiv  Detail & Related papers  (2025-07-22T10:21:55Z)
• Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels [63.415444378608214]
  Our approach incorporates innovative techniques including progressive semi-supervised learning, adaptative training strategy, and boundary enhancement. Experimental results on 3DRA datasets demonstrate the superiority of our method in terms of mesh-based segmentation metrics.
  arXiv  Detail & Related papers  (2023-08-07T14:16:52Z)
• Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation [10.18427897663732]
  Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis. This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net) Experiments on LA and Pancreas-CT dataset illustrate that DC-Net outperforms other state-of-the-art methods for semi-supervised segmentation.
  arXiv  Detail & Related papers  (2023-05-25T16:23:39Z)
• 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)
• 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)
• Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference [28.409679398886304]
  We develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets.
  arXiv  Detail & Related papers  (2022-05-06T08:37:02Z)
• 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)
• Semi-supervised Left Atrium Segmentation with Mutual Consistency Training [60.59108570938163]
  We propose a novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images. Our MC-Net consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoders are transformed as an unsupervised loss. We evaluate our MC-Net on the public Left Atrium (LA) database and it obtains impressive performance gains by exploiting the unlabeled data effectively.
  arXiv  Detail & Related papers  (2021-03-04T09:34:32Z)
• Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning [66.36706284671291]
  We propose a data-driven framework supervised by only image-level labels to support unbiased lesion localisation. The framework can explicitly separate potential lesions from original images, with the help of a generative adversarial network and a lesion-specific decoder.
  arXiv  Detail & Related papers  (2021-03-01T06:05:49Z)
• 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)
• Realistic Adversarial Data Augmentation for MR Image Segmentation [17.951034264146138]
  We propose an adversarial data augmentation method for training neural networks for medical image segmentation. Our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. We show that such an approach can improve the ability generalization and robustness of models as well as provide significant improvements in low-data scenarios.
  arXiv  Detail & Related papers  (2020-06-23T20:43:18Z)

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.