Adaptive Label Correction for Robust Medical Image Segmentation with Noisy Labels

• URL: http://arxiv.org/abs/2503.12218v1
• Date: Sat, 15 Mar 2025 18:03:01 GMT
• Title: Adaptive Label Correction for Robust Medical Image Segmentation with Noisy Labels
• Authors: Chengxuan Qian, Kai Han, Siqi Ma, Chongwen Lyu, Zhenlong Yuan, Jun Chen, Zhe Liu,
• Abstract summary: We propose a Mean Teacher-based Adaptive Label Correction framework for robust medical image segmentation with noisy labels.<n>It includes an adaptive label refinement mechanism that dynamically captures and weights differences across multiple disturbance versions to enhance the quality of noisy labels.<n>It also incorporates a sample-level uncertainty-based label selection algorithm to prioritize high-confidence samples for network updates.
• Score: 21.12128358750749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has shown remarkable success in medical image analysis, but its reliance on large volumes of high-quality labeled data limits its applicability. While noisy labeled data are easier to obtain, directly incorporating them into training can degrade model performance. To address this challenge, we propose a Mean Teacher-based Adaptive Label Correction (ALC) self-ensemble framework for robust medical image segmentation with noisy labels. The framework leverages the Mean Teacher architecture to ensure consistent learning under noise perturbations. It includes an adaptive label refinement mechanism that dynamically captures and weights differences across multiple disturbance versions to enhance the quality of noisy labels. Additionally, a sample-level uncertainty-based label selection algorithm is introduced to prioritize high-confidence samples for network updates, mitigating the impact of noisy annotations. Consistency learning is integrated to align the predictions of the student and teacher networks, further enhancing model robustness. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed framework, showing significant improvements in segmentation performance. By fully exploiting the strengths of the Mean Teacher structure, the ALC framework effectively processes noisy labels, adapts to challenging scenarios, and achieves competitive results compared to state-of-the-art methods.

Related papers

• Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation [10.40198497843647]
  Some lesion regions in medical images have unclear boundaries, irregular shapes, and small tissue density differences, leading to label ambiguity.<n>The existing model treats all data equally without taking quality differences into account in the training process.<n>A data-driven alternating learning paradigm is proposed to optimize the model's training process, achieving stable and high-precision segmentation.
  arXiv  Detail & Related papers  (2025-03-14T07:08:22Z)
• Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised Pretraining with Pseudo-Label Refinement [3.272177633069322]
  Real-world datasets often contain noisy labels due to human error, ambiguity, or resource constraints during the annotation process.<n>We propose a novel framework that combines self-supervised learning using SimCLR with iterative pseudo-label refinement.<n>Our approach significantly outperforms several state-of-the-art methods, particularly under high noise conditions.
  arXiv  Detail & Related papers  (2024-12-06T09:56:49Z)
• Vision-Language Models are Strong Noisy Label Detectors [76.07846780815794]
  This paper presents a Denoising Fine-Tuning framework, called DeFT, for adapting vision-language models. DeFT utilizes the robust alignment of textual and visual features pre-trained on millions of auxiliary image-text pairs to sieve out noisy labels. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DeFT in both noisy label detection and image classification.
  arXiv  Detail & Related papers  (2024-09-29T12:55:17Z)
• ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance [53.73316938815873]
  We propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE) to learn representations with error tolerance. ERASE combines prototype pseudo-labels with propagated denoised labels and updates representations with error resilience. Our method can outperform multiple baselines with clear margins in broad noise levels and enjoy great scalability.
  arXiv  Detail & Related papers  (2023-12-13T17:59:07Z)
• SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning [101.86916775218403]
  This paper revisits the popular pseudo-labeling methods via a unified sample weighting formulation. We propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.
  arXiv  Detail & Related papers  (2023-01-26T03:53:25Z)
• Label Noise-Robust Learning using a Confidence-Based Sieving Strategy [15.997774467236352]
  In learning tasks with label noise, improving model robustness against overfitting is a pivotal challenge. Identifying the samples with noisy labels and preventing the model from learning them is a promising approach to address this challenge. We propose a novel discriminator metric called confidence error and a sieving strategy called CONFES to differentiate between the clean and noisy samples effectively.
  arXiv  Detail & Related papers  (2022-10-11T10:47:28Z)
• Transductive CLIP with Class-Conditional Contrastive Learning [68.51078382124331]
  We propose Transductive CLIP, a novel framework for learning a classification network with noisy labels from scratch. A class-conditional contrastive learning mechanism is proposed to mitigate the reliance on pseudo labels. ensemble labels is adopted as a pseudo label updating strategy to stabilize the training of deep neural networks with noisy labels.
  arXiv  Detail & Related papers  (2022-06-13T14:04:57Z)
• Constrained Instance and Class Reweighting for Robust Learning under Label Noise [20.30785186456126]
  We propose a principled approach for tackling label noise with the aim of assigning importance weights to individual instances and class labels. Our method works by formulating a class of constrained optimization problems that yield simple closed form updates for these importance weights. We evaluate our method on several benchmark datasets and observe considerable performance gains in the presence of label noise.
  arXiv  Detail & Related papers  (2021-11-09T21:37:53Z)
• A Framework using Contrastive Learning for Classification with Noisy Labels [1.2891210250935146]
  We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies such as pseudo-labeling, sample selection with Gaussian Mixture models, weighted supervised contrastive learning have been combined into a fine-tuning phase following the pre-training.
  arXiv  Detail & Related papers  (2021-04-19T18:51:22Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)
• Learning Not to Learn in the Presence of Noisy Labels [104.7655376309784]
  We show that a new class of loss functions called the gambler's loss provides strong robustness to label noise across various levels of corruption. We show that training with this loss function encourages the model to "abstain" from learning on the data points with noisy labels.
  arXiv  Detail & Related papers  (2020-02-16T09:12:27Z)

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.