Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification

• URL: http://arxiv.org/abs/2209.01858v1
• Date: Mon, 5 Sep 2022 09:28:31 GMT
• Title: Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification
• Authors: Shafa Balaram, Cuong M. Nguyen, Ashraf Kassim, Pavitra Krishnaswamy
• Abstract summary: We introduce a novel Consistency-based Semi-supervised Evidential Active Learning framework (CSEAL) We leverage predictive uncertainty based on theories of evidence and subjective logic to develop an end-to-end integrated approach. Our approach can substantially improve accuracy on rarer abnormalities with fewer labelled samples.
• Score: 2.3545156585418328
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning approaches achieve state-of-the-art performance for classifying radiology images, but rely on large labelled datasets that require resource-intensive annotation by specialists. Both semi-supervised learning and active learning can be utilised to mitigate this annotation burden. However, there is limited work on combining the advantages of semi-supervised and active learning approaches for multi-label medical image classification. Here, we introduce a novel Consistency-based Semi-supervised Evidential Active Learning framework (CSEAL). Specifically, we leverage predictive uncertainty based on theories of evidence and subjective logic to develop an end-to-end integrated approach that combines consistency-based semi-supervised learning with uncertainty-based active learning. We apply our approach to enhance four leading consistency-based semi-supervised learning methods: Pseudo-labelling, Virtual Adversarial Training, Mean Teacher and NoTeacher. Extensive evaluations on multi-label Chest X-Ray classification tasks demonstrate that CSEAL achieves substantive performance improvements over two leading semi-supervised active learning baselines. Further, a class-wise breakdown of results shows that our approach can substantially improve accuracy on rarer abnormalities with fewer labelled samples.

Related papers

• Multi-Agent Reinforcement Learning from Human Feedback: Data Coverage and Algorithmic Techniques [65.55451717632317]
  We study Multi-Agent Reinforcement Learning from Human Feedback (MARLHF), exploring both theoretical foundations and empirical validations. We define the task as identifying Nash equilibrium from a preference-only offline dataset in general-sum games. Our findings underscore the multifaceted approach required for MARLHF, paving the way for effective preference-based multi-agent systems.
  arXiv  Detail & Related papers  (2024-09-01T13:14:41Z)
• OTMatch: Improving Semi-Supervised Learning with Optimal Transport [2.4355694259330467]
  We present a new approach called OTMatch, which leverages semantic relationships among classes by employing an optimal transport loss function to match distributions. The empirical results show improvements in our method above baseline, this demonstrates the effectiveness and superiority of our approach in harnessing semantic relationships to enhance learning performance in a semi-supervised setting.
  arXiv  Detail & Related papers  (2023-10-26T15:01:54Z)
• Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation [14.536384387956527]
  We develop a novel Multi-Scale Cross Supervised Contrastive Learning framework to segment structures in medical images. Our approach contrasts multi-scale features based on ground-truth and cross-predicted labels, in order to extract robust feature representations. It outperforms state-of-the-art semi-supervised methods by more than 3.0% in Dice.
  arXiv  Detail & Related papers  (2023-06-25T16:55:32Z)
• Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation [10.18427897663732]
  Consistency learning plays a crucial role in semi-supervised medical image segmentation. It enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. We propose a self-aware and cross-sample prototypical learning method ( SCP-Net) to enhance the diversity of prediction in consistency learning.
  arXiv  Detail & Related papers  (2023-05-25T16:22:04Z)
• Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification [52.94051907952536]
  We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
  arXiv  Detail & Related papers  (2022-07-14T14:57:01Z)
• Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation [9.745971699005857]
  We propose a novel uncertainty-guided mutual consistency learning framework for medical image segmentation. It integrates intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information. Our method achieves performance gains by leveraging unlabeled data and outperforms existing semi-supervised segmentation methods.
  arXiv  Detail & Related papers  (2021-12-05T08:19:41Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)
• MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
  Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition. We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL) In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
  arXiv  Detail & Related papers  (2021-07-23T06:57:08Z)
• Robust Pre-Training by Adversarial Contrastive Learning [120.33706897927391]
  Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness. We improve robustness-aware self-supervised pre-training by learning representations consistent under both data augmentations and adversarial perturbations.
  arXiv  Detail & Related papers  (2020-10-26T04:44:43Z)
• Confident Coreset for Active Learning in Medical Image Analysis [57.436224561482966]
  We propose a novel active learning method, confident coreset, which considers both uncertainty and distribution for effectively selecting informative samples. By comparative experiments on two medical image analysis tasks, we show that our method outperforms other active learning methods.
  arXiv  Detail & Related papers  (2020-04-05T13:46:16Z)

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.