Self-supervised learning methods and applications in medical imaging analysis: A survey

• URL: http://arxiv.org/abs/2109.08685v1
• Date: Fri, 17 Sep 2021 17:01:42 GMT
• Title: Self-supervised learning methods and applications in medical imaging analysis: A survey
• Authors: Saeed Shurrab, Rehab Duwiari
• Abstract summary: The article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with concentration on their applications in the field of medical imaging analysis. The article covers (40) of the most recent researches in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The availability of high quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered as an effective solution for the scarcity in annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative and contrastive approaches. Moreover, the article covers (40) of the most recent researches in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Ultimately, the article concludes with possible future research directions in the field.

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)
• A comprehensive survey on deep active learning in medical image analysis [23.849628978883707]
  Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible.
  arXiv  Detail & Related papers  (2023-10-22T08:46:40Z)
• A Systematic Review of Few-Shot Learning in Medical Imaging [1.049712834719005]
  Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis. This systematic review gives a comprehensive overview of few-shot learning in medical imaging.
  arXiv  Detail & Related papers  (2023-09-20T16:10:53Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Federated Learning for Medical Image Analysis: A Survey [16.800565615106784]
  Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently.
  arXiv  Detail & Related papers  (2023-06-09T15:46:42Z)
• Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges [64.63744409431001]
  We present a comprehensive survey on advances in adversarial attacks and defenses for medical image analysis. For a fair comparison, we establish a new benchmark for adversarially robust medical diagnosis models.
  arXiv  Detail & Related papers  (2023-03-24T16:38:58Z)
• A Trustworthy Framework for Medical Image Analysis with Deep Learning [71.48204494889505]
  TRUDLMIA is a trustworthy deep learning framework for medical image analysis. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
  arXiv  Detail & Related papers  (2022-12-06T05:30:22Z)
• Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions [1.9065960619519515]
  Reinforcement learning has gradually gained momentum in recent years. Many researchers in the medical analysis field find it hard to understand and deploy in clinics. This paper may help the readers to learn how to formulate and solve their medical image analysis research as reinforcement learning problems.
  arXiv  Detail & Related papers  (2022-06-28T22:07:17Z)
• Recent advances and clinical applications of deep learning in medical image analysis [7.132678647070632]
  We reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images.
  arXiv  Detail & Related papers  (2021-05-27T18:05:12Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Domain Shift in Computer Vision models for MRI data analysis: An Overview [64.69150970967524]
  Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yet only a few applications are now in clinical use. Poor transferability of themodels to data from different sources or acquisition domains is one of the reasons for that.
  arXiv  Detail & Related papers  (2020-10-14T16:34:21Z)

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.