Fully Differentiable Bidirectional Dual-Task Synergistic Learning for Semi-Supervised 3D Medical Image Segmentation

• URL: http://arxiv.org/abs/2602.09378v1
• Date: Tue, 10 Feb 2026 03:44:24 GMT
• Title: Fully Differentiable Bidirectional Dual-Task Synergistic Learning for Semi-Supervised 3D Medical Image Segmentation
• Authors: Jun Li,
• Abstract summary: Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data.<n>We propose a fully Differentiable Bidirectional Synergistic Learning (DBiSL) framework, which seamlessly integrates and enhances four critical SSL components.
• Score: 3.9950415168730107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. The scarcity of high-quality labeled data remains a major challenge in medical image analysis due to the high annotation costs and the need for specialized clinical expertise. Semi-supervised learning has demonstrated significant potential in addressing this bottleneck, with pseudo-labeling and consistency regularization emerging as two predominant paradigms. Dual-task collaborative learning, an emerging consistency-aware paradigm, seeks to derive supplementary supervision by establishing prediction consistency between related tasks. However, current methodologies are limited to unidirectional interaction mechanisms (typically regression-to-segmentation), as segmentation results can only be transformed into regression outputs in an offline manner, thereby failing to fully exploit the potential benefits of online bidirectional cross-task collaboration. Thus, we propose a fully Differentiable Bidirectional Synergistic Learning (DBiSL) framework, which seamlessly integrates and enhances four critical SSL components: supervised learning, consistency regularization, pseudo-supervised learning, and uncertainty estimation. Experiments on two benchmark datasets demonstrate our method's state-of-the-art performance. Beyond technical contributions, this work provides new insights into unified SSL framework design and establishes a new architectural foundation for dual-task-driven SSL, while offering a generic multitask learning framework applicable to broader computer vision applications. The code will be released on github upon acceptance.

Related papers

• Revisiting Multi-Task Visual Representation Learning [52.93947931352643]
  We introduce MTV, a principled multi-task visual pretraining framework.<n>We leverage high-capacity "expert" models to synthesize dense, structured pseudo-labels at scale.<n>Our results demonstrate that MTV achieves "best-of-both-worlds" performance.
  arXiv  Detail & Related papers  (2026-01-20T11:59:19Z)
• CLIP-Guided Adaptable Self-Supervised Learning for Human-Centric Visual Tasks [76.00315860962885]
  We propose CLASP (CLIP-guided Adaptable Self-suPervised learning), a novel framework for unsupervised pre-training in human-centric visual tasks.<n> CLASP leverages the powerful vision-language model CLIP to generate both low-level (e.g., body parts) and high-level (e.g., attributes) semantic pseudo-labels.<n>MoE dynamically adapts feature extraction based on task-specific prompts, mitigating potential feature conflicts and enhancing transferability.
  arXiv  Detail & Related papers  (2026-01-19T15:19:28Z)
• Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion [31.189038928192648]
  Co2S is a semi-supervised RS segmentation framework that fuses priors from vision-language models and self-supervised models.<n>An explicit-implicit semantic co-guidance mechanism is introduced that utilizes text embeddings and learnable queries.<n>Experiments on six popular datasets demonstrate the superiority of the proposed method.
  arXiv  Detail & Related papers  (2025-12-28T18:24:19Z)
• Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification [59.59359638389348]
  We propose a Dual-level Modality Debiasing Learning framework that implements debiasing at both the model and optimization levels.<n>Experiments on benchmark datasets demonstrate that DMDL could enable modality-invariant feature learning and a more generalized model.
  arXiv  Detail & Related papers  (2025-12-03T12:43:16Z)
• AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading [0.0]
  Embryo fragmentation is a morphological indicator critical for evaluating developmental potential in In Vitro Fertilization (IVF)<n>Existing deep learning solutions often lack clinical explainability or suffer from accumulated errors in segmentation area estimation.<n>This study proposes AttnRegDeepLab, a framework characterized by dual-branch Multi-Task Learning.
  arXiv  Detail & Related papers  (2025-11-23T13:50:49Z)
• MEJO: MLLM-Engaged Surgical Triplet Recognition via Inter- and Intra-Task Joint Optimization [52.149337961205624]
  We propose a framework that empowers both inter- and intra-task optimization for surgical triplet recognition.<n>For inter-task optimization, we introduce the Shared-Specific-Disentangled (S$2$D) learning scheme that decomposes representations into task-shared and task-specific components.<n>For intra-task optimization conflicts, we develop a Coordinated Gradient Learning (CGL) strategy, which dissects and rebalances the positive-negative ambiguities.
  arXiv  Detail & Related papers  (2025-09-16T09:48:52Z)
• Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation [27.94353306813293]
  Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data.<n>Main challenge in the design of semi-supervised learning methods is the effective use of the unlabelled data for training.<n>We introduce a new methodology to produce high-quality pseudo-labels for a consistency learning strategy.
  arXiv  Detail & Related papers  (2025-02-17T05:29:50Z)
• Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning [1.5233179662962222]
  The segmentation of the pubic symphysis and fetal head (PSFH) constitutes a pivotal step in monitoring labor progression and identifying potential delivery complications. Traditional semi-supervised learning approaches primarily utilize a unified network model based on Convolutional Neural Networks (CNNs) We introduce a novel framework, the Dual-Student and Teacher Combining CNN and Transformer (DSTCT)
  arXiv  Detail & Related papers  (2024-09-11T00:57:31Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• Robust Training of Federated Models with Extremely Label Deficiency [84.00832527512148]
  Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. We propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings.
  arXiv  Detail & Related papers  (2024-02-22T10:19:34Z)
• Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation [10.18427897663732]
  Consistency learning plays a crucial role in semi-supervised medical image segmentation. It enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. We propose a self-aware and cross-sample prototypical learning method ( SCP-Net) to enhance the diversity of prediction in consistency learning.
  arXiv  Detail & Related papers  (2023-05-25T16:22:04Z)
• Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation [12.940103904327655]
  We propose a novel dual-task mutual learning framework for semi-supervised medical image segmentation. Our framework can be formulated as an integration of two individual segmentation networks based on two tasks. By jointly learning the segmentation probability maps and signed distance maps of targets, our framework can enforce the geometric shape constraint and learn more reliable information.
  arXiv  Detail & Related papers  (2021-03-08T12:38:23Z)

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.