Continual Learning in Medical Imaging: A Survey and Practical Analysis

• URL: http://arxiv.org/abs/2405.13482v2
• Date: Tue, 01 Oct 2024 08:04:42 GMT
• Title: Continual Learning in Medical Imaging: A Survey and Practical Analysis
• Authors: Mohammad Areeb Qazi, Anees Ur Rehman Hashmi, Santosh Sanjeev, Ibrahim Almakky, Numan Saeed, Camila Gonzalez, Mohammad Yaqub,
• Abstract summary: Continual Learning offers promise in enabling the sequential acquisition of new knowledge without forgetting previous learnings in neural networks. We review the recent literature on continual learning in the medical domain, highlight recent trends, and point out the practical issues. We critically discuss the current state of continual learning in medical imaging, including identifying open problems and outlining promising future directions.
• Score: 0.7794090852114541
• License:
• Abstract: Deep Learning has shown great success in reshaping medical imaging, yet it faces numerous challenges hindering widespread application. Issues like catastrophic forgetting and distribution shifts in the continuously evolving data stream increase the gap between research and applications. Continual Learning offers promise in addressing these hurdles by enabling the sequential acquisition of new knowledge without forgetting previous learnings in neural networks. In this survey, we comprehensively review the recent literature on continual learning in the medical domain, highlight recent trends, and point out the practical issues. Specifically, we survey the continual learning studies on classification, segmentation, detection, and other tasks in the medical domain. Furthermore, we develop a taxonomy for the reviewed studies, identify the challenges, and provide insights to overcome them. We also critically discuss the current state of continual learning in medical imaging, including identifying open problems and outlining promising future directions. We hope this survey will provide researchers with a useful overview of the developments in the field and will further increase interest in the community. To keep up with the fast-paced advancements in this field, we plan to routinely update the repository with the latest relevant papers at https://github.com/BioMedIA-MBZUAI/awesome-cl-in-medical .

Related papers

• A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond [84.95530356322621]
  This survey presents a systematic review of the advancements in code intelligence. It covers over 50 representative models and their variants, more than 20 categories of tasks, and an extensive coverage of over 680 related works. Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence.
  arXiv  Detail & Related papers  (2024-03-21T08:54:56Z)
• A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning [58.107474025048866]
  Forgetting refers to the loss or deterioration of previously acquired knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning.
  arXiv  Detail & Related papers  (2023-07-16T16:27:58Z)
• Medical image registration using unsupervised deep neural network: A scoping literature review [0.9527960631238173]
  In medicine, image registration is vital in image-guided interventions and other clinical applications. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy.
  arXiv  Detail & Related papers  (2022-08-03T03:11:34Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• Transformers in Medical Imaging: A Survey [88.03790310594533]
  Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results. Medical imaging has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. We provide a review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues.
  arXiv  Detail & Related papers  (2022-01-24T18:50:18Z)
• Deep Long-Tailed Learning: A Survey [163.16874896812885]
  Deep long-tailed learning aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution. Long-tailed class imbalance is a common problem in practical visual recognition tasks. This paper provides a comprehensive survey on recent advances in deep long-tailed learning.
  arXiv  Detail & Related papers  (2021-10-09T15:25:22Z)
• A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises [27.16172003905426]
  We first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging. We then present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging.
  arXiv  Detail & Related papers  (2020-08-02T14:26:13Z)
• Deep Learning for Anomaly Detection: A Review [150.9270911031327]
  This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges.
  arXiv  Detail & Related papers  (2020-07-06T02:21:16Z)
• A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [38.90186125141749]
  Small size of medical datasets remains a major bottleneck in deep learning. Traditional approaches leverage the information from natural images via transfer learning. More recent works utilize the domain knowledge from medical doctors to create networks that resemble how medical doctors are trained.
  arXiv  Detail & Related papers  (2020-04-25T14:27:47Z)
• Medical Image Registration Using Deep Neural Networks: A Comprehensive Review [1.2425910171551517]
  State-of-the-art literature on medical image registration using deep neural networks is presented. The review is systematic and encompasses all the related works previously published in the field. This review allows a deep understanding and insight for the readers active in the field who are investigating the state-of-the-art and seeking to contribute the future literature.
  arXiv  Detail & Related papers  (2020-02-09T17:22:05Z)

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.