ACPL: Anti-curriculum Pseudo-labelling forSemi-supervised Medical Image Classification

• URL: http://arxiv.org/abs/2111.12918v1
• Date: Thu, 25 Nov 2021 05:31:52 GMT
• Title: ACPL: Anti-curriculum Pseudo-labelling forSemi-supervised Medical Image Classification
• Authors: Fengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, Gustavo Carneiro
• Abstract summary: We propose a new SSL algorithm, called anti-curriculum pseudo-labelling (ACPL) ACPL introduces novel techniques to select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems. Our method outperforms previous SOTA SSL methods on both datasets.
• Score: 22.5935068122522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective semi-supervised learning (SSL) in medical im-age analysis (MIA) must address two challenges: 1) workeffectively on both multi-class (e.g., lesion classification)and multi-label (e.g., multiple-disease diagnosis) problems,and 2) handle imbalanced learning (because of the highvariance in disease prevalence). One strategy to explorein SSL MIA is based on the pseudo labelling strategy, butit has a few shortcomings. Pseudo-labelling has in generallower accuracy than consistency learning, it is not specifi-cally design for both multi-class and multi-label problems,and it can be challenged by imbalanced learning. In this paper, unlike traditional methods that select confident pseudo label by threshold, we propose a new SSL algorithm, called anti-curriculum pseudo-labelling (ACPL), which introduces novel techniques to select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems, and to estimate pseudo labels by an accurate ensemble of classifiers(improving pseudo label accuracy). We run extensive experiments to evaluate ACPL on two public medical image classification benchmarks: Chest X-Ray14 for thorax disease multi-label classification and ISIC2018 for skin lesion multi-class classification. Our method outperforms previous SOTA SSL methods on both datasets.

Related papers

• FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity [30.49607763632271]
  Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy. We propose a two-stage method FedMLP to combat class missing from two aspects: pseudo label tagging and global knowledge learning. Experiments on two publicly-available medical datasets validate the superiority of FedMLP against the state-of-the-art both federated semi-supervised and noisy label learning approaches.
  arXiv  Detail & Related papers  (2024-06-27T08:36:43Z)
• A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification [7.619591696318021]
  Immune repertoire classification is a frontier research topic in computational biology. We propose a noisy-label-learning formulation to solve the immune repertoire classification task. Experiments on the Cytomegalovirus (CMV) and Cancer datasets demonstrate our method's effectiveness and superior performance.
  arXiv  Detail & Related papers  (2023-07-29T09:19:27Z)
• SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification [11.435826510575879]
  Semi-supervised learning (SSL) methods can mitigate challenges by leveraging both labeled and unlabeled data. SSL methods for medical image classification need to address two key challenges: (1) estimating reliable pseudo-labels for the images in the unlabeled dataset and (2) reducing biases caused by class imbalance. In this paper, we propose a novel SSL approach, SPLAL, that effectively addresses these challenges.
  arXiv  Detail & Related papers  (2023-07-10T14:53:24Z)
• Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning [97.88458953075205]
  Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
  arXiv  Detail & Related papers  (2023-05-04T12:52:18Z)
• Universal Semi-Supervised Learning for Medical Image Classification [21.781201758182135]
  Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data. Traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution. We propose a unified framework to leverage unseen unlabeled data for open-scenario semi-supervised medical image classification.
  arXiv  Detail & Related papers  (2023-04-08T16:12:36Z)
• Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection [98.66771688028426]
  We propose a Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors. Joint-Confidence Estimation (JCE) is proposed to quantifies the classification and localization quality of pseudo labels. ARSL effectively mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS COCO and PASCAL VOC.
  arXiv  Detail & Related papers  (2023-03-27T07:46:58Z)
• PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification [64.39761523935613]
  We propose a percentile-based threshold adjusting scheme to dynamically alter the score thresholds of positive and negative pseudo-labels for each class during the training. We achieve strong performance on Pascal VOC2007 and MS-COCO datasets when compared to recent SSL methods.
  arXiv  Detail & Related papers  (2022-08-30T01:27:48Z)
• Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift [24.988087560120366]
  We propose the first method to tackle label shift for medical image classification. Our method effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution. We validate our method on two important medical image classification tasks including liver fibrosis staging and COVID-19 severity prediction.
  arXiv  Detail & Related papers  (2022-07-02T07:55:23Z)
• On Non-Random Missing Labels in Semi-Supervised Learning [114.62655062520425]
  Semi-Supervised Learning (SSL) is fundamentally a missing label problem. We explicitly incorporate "class" into SSL. Our method not only significantly outperforms existing baselines but also surpasses other label bias removal SSL methods.
  arXiv  Detail & Related papers  (2022-06-29T22:01:29Z)
• Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning [80.05441565830726]
  This paper addresses imbalanced semi-supervised learning, where heavily biased pseudo-labels can harm the model performance. We propose a general pseudo-labeling framework to address the bias motivated by this observation. We term the novel pseudo-labeling framework for imbalanced SSL as Distribution-Aware Semantics-Oriented (DASO) Pseudo-label.
  arXiv  Detail & Related papers  (2021-06-10T11:58:25Z)
• In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning [53.1047775185362]
  Pseudo-labeling (PL) is a general SSL approach that does not have this constraint but performs relatively poorly in its original formulation. We argue that PL underperforms due to the erroneous high confidence predictions from poorly calibrated models. We propose an uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo labeling accuracy by drastically reducing the amount of noise encountered in the training process.
  arXiv  Detail & Related papers  (2021-01-15T23:29:57Z)

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.