Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification

• URL: http://arxiv.org/abs/2406.12293v2
• Date: Wed, 30 Oct 2024 03:11:52 GMT
• Title: Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification
• Authors: Zehui Liao, Shishuai Hu, Yanning Zhang, Yong Xia,
• Abstract summary: We propose the Extended Noise-robust Contrastive and Open-set Feature Augmentation framework for medical image classification tasks. This framework incorporates the Extended Noise-robust Supervised Contrastive Loss, which helps differentiate features among both in-distribution and out-of-distribution classes. We also develop the Open-set Feature Augmentation module that enriches open-set samples at the feature level and then assigns them dynamic class labels.
• Score: 45.319828759068415
• License:
• Abstract: Addressing mixed closed-set and open-set label noise in medical image classification remains a largely unexplored challenge. Unlike natural image classification, which often separates and processes closed-set and open-set noisy samples from clean ones, medical image classification contends with high inter-class similarity, complicating the identification of open-set noisy samples. Additionally, existing methods often fail to fully utilize open-set noisy samples for label noise mitigation, leading to their exclusion or the application of uniform soft labels. To address these challenges, we propose the Extended Noise-robust Contrastive and Open-set Feature Augmentation framework for medical image classification tasks. This framework incorporates the Extended Noise-robust Supervised Contrastive Loss, which helps differentiate features among both in-distribution and out-of-distribution classes. This loss treats open-set noisy samples as an extended class, improving label noise mitigation by weighting contrastive pairs according to label reliability. Additionally, we develop the Open-set Feature Augmentation module that enriches open-set samples at the feature level and then assigns them dynamic class labels, thereby leveraging the model's capacity and reducing overfitting to noisy data. We evaluated the proposed framework on both a synthetic noisy dataset and a real-world noisy dataset. The results indicate the superiority of our framework over four existing methods and the effectiveness of leveraging open-set noisy samples to combat label noise.

Related papers

• Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise [10.232537737211098]
  We propose a two-phase approach that combines Learning with Noisy Labels (LNL) and active learning. We demonstrate that our proposed technique is superior to its predecessors at handling class imbalance by not misidentifying clean samples from minority classes as mostly noisy samples.
  arXiv  Detail & Related papers  (2024-07-08T14:16:05Z)
• Extracting Clean and Balanced Subset for Noisy Long-tailed Classification [66.47809135771698]
  We develop a novel pseudo labeling method using class prototypes from the perspective of distribution matching. By setting a manually-specific probability measure, we can reduce the side-effects of noisy and long-tailed data simultaneously. Our method can extract this class-balanced subset with clean labels, which brings effective performance gains for long-tailed classification with label noise.
  arXiv  Detail & Related papers  (2024-04-10T07:34:37Z)
• Group Benefits Instances Selection for Data Purification [21.977432359384835]
  Existing methods for combating label noise are typically designed and tested on synthetic datasets. We propose a method named GRIP to alleviate the noisy label problem for both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-23T03:06:19Z)
• Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels [78.88007892742438]
  Training multi-label models with partial positive labels (MLR-PPL) attracts increasing attention. Previous works regard unknown labels as negative and adopt traditional MLR algorithms. We propose to explore semantic correlation among different images to facilitate the MLR-PPL task.
  arXiv  Detail & Related papers  (2022-11-15T02:11:20Z)
• Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling [22.62790706276081]
  Training deep neural network (DNN) with noisy labels is practically challenging. Previous efforts tend to handle part or full data in a unified denoising flow. We propose a coarse-to-fine robust learning method called CREMA to handle noisy data in a divide-and-conquer manner.
  arXiv  Detail & Related papers  (2022-08-23T02:06:38Z)
• Neighborhood Collective Estimation for Noisy Label Identification and Correction [92.20697827784426]
  Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias. We propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors.
  arXiv  Detail & Related papers  (2022-08-05T14:47:22Z)
• S3: Supervised Self-supervised Learning under Label Noise [53.02249460567745]
  In this paper we address the problem of classification in the presence of label noise. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space. Our method significantly surpasses previous methods on both CIFARCIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.
  arXiv  Detail & Related papers  (2021-11-22T15:49:20Z)
• Open-set Label Noise Can Improve Robustness Against Inherent Label Noise [27.885927200376386]
  We show that open-set noisy labels can be non-toxic and even benefit the robustness against inherent noisy labels. We propose a simple yet effective regularization by introducing Open-set samples with Dynamic Noisy Labels (ODNL) into training.
  arXiv  Detail & Related papers  (2021-06-21T07:15:50Z)
• 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)

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.