Pathological Image Segmentation with Noisy Labels

• URL: http://arxiv.org/abs/2104.02602v1
• Date: Sat, 20 Mar 2021 03:36:06 GMT
• Title: Pathological Image Segmentation with Noisy Labels
• Authors: Li Xiao, Yinhao Li, Luxi Qv, Xinxia Tian, Yijie Peng, S.Kevin Zhou
• Abstract summary: We propose a novel label re-weighting framework to account for the reliability of different experts' labels on each pixel. We also devise a new attention heatmap, which takes roughness as prior knowledge to guide the model to focus on important regions.
• Score: 13.8002043402326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmentation of pathological images is essential for accurate disease diagnosis. The quality of manual labels plays a critical role in segmentation accuracy; yet, in practice, the labels between pathologists could be inconsistent, thus confusing the training process. In this work, we propose a novel label re-weighting framework to account for the reliability of different experts' labels on each pixel according to its surrounding features. We further devise a new attention heatmap, which takes roughness as prior knowledge to guide the model to focus on important regions. Our approach is evaluated on the public Gleason 2019 datasets. The results show that our approach effectively improves the model's robustness against noisy labels and outperforms state-of-the-art approaches.

Related papers

• Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels [33.676420623855314]
  Medical image segmentation is crucial in the field of medical imaging, aiding in disease diagnosis and surgical planning. Most established segmentation methods rely on supervised deep learning, in which clean and precise labels are essential for supervision. We propose a deep self-cleansing segmentation framework that can preserve clean labels while cleansing noisy ones in the training phase.
  arXiv  Detail & Related papers  (2024-09-08T08:33:32Z)
• Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model [7.021021047695508]
  We propose a contrast-based variational model to generate segmentation results for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled novel' regions.
  arXiv  Detail & Related papers  (2023-04-07T10:12:21Z)
• Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training [73.60883490436956]
  We propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification. We employ the self-ensemble model with a noisy label filter to efficiently select the clean and noisy samples. We also design a novel global and local representation learning scheme to implicitly regularize the networks to utilize noisy samples.
  arXiv  Detail & Related papers  (2022-05-10T07:50:08Z)
• Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation [25.76014072179711]
  We propose a novel approach to generate supervision for semi-supervised semantic segmentation. We use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantics of the best fitting pixel in a reference set. We achieve the same performance as a standard fully supervised model on X-ray anatomy segmentation, albeit 95% fewer labeled images.
  arXiv  Detail & Related papers  (2021-12-01T12:21:24Z)
• Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation [6.889911520730388]
  We aim to boost the performance of semi-supervised learning for medical image segmentation with limited labels. We learn latent representations directly at feature-level by imposing contrastive loss on unlabeled images. We conduct experiments on an MRI and a CT segmentation dataset and demonstrate that the proposed method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-27T03:27:58Z)
• Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation [61.09321488002978]
  We present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation. Our model consists of three independent network, which can effectively learn useful information from the peer networks. Our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.
  arXiv  Detail & Related papers  (2021-04-05T15:50:16Z)
• Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation [72.0276067144762]
  We discuss and define the two common types of label noise in medical images. We propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task.
  arXiv  Detail & Related papers  (2021-02-28T14:56:45Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis [102.40869566439514]
  We seek to exploit rich labeled data from relevant domains to help the learning in the target task via Unsupervised Domain Adaptation (UDA) Unlike most UDA methods that rely on clean labeled data or assume samples are equally transferable, we innovatively propose a Collaborative Unsupervised Domain Adaptation algorithm. We theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images.
  arXiv  Detail & Related papers  (2020-07-05T11:49:17Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Manifold-driven Attention Maps for Weakly Supervised Segmentation [9.289524646688244]
  We propose a manifold driven attention-based network to enhance visual salient regions. Our method generates superior attention maps directly during inference without the need of extra computations.
  arXiv  Detail & Related papers  (2020-04-07T00:03:28Z)

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.