Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition

• URL: http://arxiv.org/abs/2404.10405v1
• Date: Tue, 16 Apr 2024 09:12:16 GMT
• Title: Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition
• Authors: Hao Feng, Yuanzhe Jia, Ruijia Xu, Mukesh Prasad, Ali Anaissi, Ali Braytee,
• Abstract summary: We propose an innovative approach by integrating self-supervised learning into semi-supervised models to enhance medical image recognition. Our approach optimally leverages unlabeled data, outperforming existing methods in terms of accuracy for medical image recognition.
• Score: 10.317372960942972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image recognition techniques heavily rely on abundant labeled data, particularly in medical contexts. Addressing the challenges associated with obtaining labeled data has led to the prominence of self-supervised learning and semi-supervised learning, especially in scenarios with limited annotated data. In this paper, we proposed an innovative approach by integrating self-supervised learning into semi-supervised models to enhance medical image recognition. Our methodology commences with pre-training on unlabeled data utilizing the BYOL method. Subsequently, we merge pseudo-labeled and labeled datasets to construct a neural network classifier, refining it through iterative fine-tuning. Experimental results on three different datasets demonstrate that our approach optimally leverages unlabeled data, outperforming existing methods in terms of accuracy for medical image recognition.

Related papers

• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning [48.97640824497327]
  We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
  arXiv  Detail & Related papers  (2024-02-03T05:48:50Z)
• Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation [13.707121013895929]
  We present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation. We use distinct decoders for student and teacher networks while maintain the same encoder. To learn from unlabeled data, we create pseudo-labels generated by the teacher networks and augment the training data with the pseudo-labels.
  arXiv  Detail & Related papers  (2023-08-31T09:13:34Z)
• Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation [0.20305676256390934]
  Semi-supervised learning has the potential to improve the data-efficiency of training data-hungry deep neural networks. We present a simple semi-supervised learning method for lesion segmentation tasks based on the ideas of cut-paste augmentation and consistency regularization.
  arXiv  Detail & Related papers  (2022-10-01T04:43:54Z)
• Efficient Medical Image Assessment via Self-supervised Learning [27.969767956918503]
  High-performance deep learning methods typically rely on large annotated training datasets. We propose a novel and efficient data assessment strategy to rank the quality of unlabeled medical image data. Motivated by theoretical implication of SSL embedding space, we leverage a Masked Autoencoder for feature extraction.
  arXiv  Detail & Related papers  (2022-09-28T21:39:00Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis [64.4093648042484]
  We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data.
  arXiv  Detail & Related papers  (2022-06-01T09:20:30Z)
• Self-Paced Contrastive Learning for Semi-supervisedMedical Image Segmentation with Meta-labels [6.349708371894538]
  We propose to adapt contrastive learning to work with meta-label annotations. We use the meta-labels for pre-training the image encoder as well as to regularize a semi-supervised training. Results on three different medical image segmentation datasets show that our approach highly boosts the performance of a model trained on a few scans.
  arXiv  Detail & Related papers  (2021-07-29T04:30:46Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation [65.81546955181781]
  We propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher. The student model learns the knowledge of unlabeled target data and labeled source data by two teacher models. We demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance.
  arXiv  Detail & Related papers  (2020-07-13T10:00:44Z)
• 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)

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.