Recent advances and clinical applications of deep learning in medical image analysis

• URL: http://arxiv.org/abs/2105.13381v1
• Date: Thu, 27 May 2021 18:05:12 GMT
• Title: Recent advances and clinical applications of deep learning in medical image analysis
• Authors: Xuxin Chen, Ximin Wang, Ke Zhang, Roy Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
• Abstract summary: 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.
• Score: 7.132678647070632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has become the mainstream technology in computer vision, and it has received extensive research interest in developing new medical image processing algorithms to support disease detection and diagnosis. As compared to conventional machine learning technologies, the major advantage of deep learning is that models can automatically identify and recognize representative features through the hierarchal model architecture, while avoiding the laborious development of hand-crafted features. In this paper, 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, which are summarized based on different application scenarios, including lesion classification, segmentation, detection, and image registration. Additionally, we also discussed the major technical challenges and suggested the possible solutions in future research efforts.

Related papers

• 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)
• Computer Vision on X-ray Data in Industrial Production and Security Applications: A survey [89.45221564651145]
  This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications. It covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets.
  arXiv  Detail & Related papers  (2022-11-10T13:37:36Z)
• Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis [17.22614309681354]
  Histological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis. Deep learning methods represented by convolutional neural networks have gradually become the mainstream in the field of digital pathology. We present a comprehensive and systematic review of the latest studies on weakly supervised learning, semi-supervised learning, and self-supervised learning in the field of computational pathology.
  arXiv  Detail & Related papers  (2022-08-18T11:49:41Z)
• Self-supervised learning methods and applications in medical imaging analysis: A survey [0.0]
  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.
  arXiv  Detail & Related papers  (2021-09-17T17:01:42Z)
• Medical Imaging and Machine Learning [16.240472115235253]
  The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging. Data availability, need for novel computing architectures and explainable AI algorithms, are still relevant. In this paper we explore challenges unique to high dimensional clinical imaging data, in addition to highlighting some of the technical and ethical considerations.
  arXiv  Detail & Related papers  (2021-03-02T18:53:39Z)
• 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)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• 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)
• Image Segmentation Using Deep Learning: A Survey [58.37211170954998]
  Image segmentation is a key topic in image processing and computer vision. There has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models.
  arXiv  Detail & Related papers  (2020-01-15T21:37:47Z)
• Deep neural network models for computational histopathology: A survey [1.2891210250935146]
  deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images. In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used. We highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research.
  arXiv  Detail & Related papers  (2019-12-28T01:04:25Z)

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.