SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss

• URL: http://arxiv.org/abs/2409.18147v3
• Date: Thu, 24 Oct 2024 07:25:40 GMT
• Title: SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss
• Authors: Mengwen Ye, Yingzi Huangfu, You Li, Zekuan Yu,
• Abstract summary: Fundus image classification is crucial in the computer aided diagnosis tasks, but label noise significantly impairs the performance of deep neural networks. We propose a robust framework, Self-Supervised Pre-training with Robust Adaptive Credal Loss (SSP-RACL), for handling label noise in fundus image datasets.
• Score: 3.8739860035485143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fundus image classification is crucial in the computer aided diagnosis tasks, but label noise significantly impairs the performance of deep neural networks. To address this challenge, we propose a robust framework, Self-Supervised Pre-training with Robust Adaptive Credal Loss (SSP-RACL), for handling label noise in fundus image datasets. First, we use Masked Autoencoders (MAE) for pre-training to extract features, unaffected by label noise. Subsequently, RACL employ a superset learning framework, setting confidence thresholds and adaptive label relaxation parameter to construct possibility distributions and provide more reliable ground-truth estimates, thus effectively suppressing the memorization effect. Additionally, we introduce clinical knowledge-based asymmetric noise generation to simulate real-world noisy fundus image datasets. Experimental results demonstrate that our proposed method outperforms existing approaches in handling label noise, showing superior performance.

Related papers

• Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness [56.2479170374811]
  We introduce Fine-Tuning with Confidence-Aware Denoised Image Selection (FT-CADIS) FT-CADIS is inspired by the observation that the confidence of off-the-shelf classifiers can effectively identify hallucinated images during denoised smoothing. It has established the state-of-the-art certified robustness among denoised smoothing methods across all $ell$-adversary radius in various benchmarks.
  arXiv  Detail & Related papers  (2024-11-13T09:13:20Z)
• Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems [0.7497462432118391]
  Deep learning can be used to optimize image-based diagnostic decision support systems. The Informed Deep Abstaining (IDAC) system enhances the noise-robust Deep Abstaining (DAC) loss by incorporating noise level estimations during training. These findings are reproduced on an in-house noisy data set, where labels were extracted from the clinical systems of the University Hospital Bonn by a text-based transformer.
  arXiv  Detail & Related papers  (2024-10-28T13:36:57Z)
• Robust Learning under Hybrid Noise [24.36707245704713]
  We propose a novel unified learning framework called "Feature and Label Recovery" (FLR) to combat the hybrid noise from the perspective of data recovery.
  arXiv  Detail & Related papers  (2024-07-04T16:13:25Z)
• I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images [8.763680382529412]
  pixel-shuffle downsampling (PD) has been proposed to eliminate the spatial correlation of noise. We propose self-residual learning without the PD process to maintain texture information. The results of extensive experiments show that the proposed method outperforms state-of-the-art self-supervised blind denoising approaches.
  arXiv  Detail & Related papers  (2023-02-21T08:51:17Z)
• Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling [29.744621356187764]
  This paper presents SeqUST, a novel uncertain-aware self-training framework for Neural sequence labeling (NSL) We incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation at the token level and then select reliable language tokens from unlabeled data. A well-designed masked sequence labeling task with a noise-robust loss supports robust training, which aims to suppress the problem of noisy pseudo labels.
  arXiv  Detail & Related papers  (2023-02-17T02:40:04Z)
• NLIP: Noise-robust Language-Image Pre-training [95.13287735264937]
  We propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. Our NLIP can alleviate the common noise effects during image-text pre-training in a more efficient way.
  arXiv  Detail & Related papers  (2022-12-14T08:19:30Z)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction [109.44933866397123]
  Noise robustness is essential for deploying automatic speech recognition systems in real-world environments. We employ a noise-robust representation learned by a refined self-supervised framework for noisy speech recognition. We achieve comparable performance to the best supervised approach reported with only 16% of labeled data.
  arXiv  Detail & Related papers  (2021-10-28T20:39:02Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)
• 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)

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.