Data efficient deep learning for medical image analysis: A survey

• URL: http://arxiv.org/abs/2310.06557v1
• Date: Tue, 10 Oct 2023 12:13:38 GMT
• Title: Data efficient deep learning for medical image analysis: A survey
• Authors: Suruchi Kumari and Pravendra Singh
• Abstract summary: The rapid evolution of deep learning has significantly advanced the field of medical image analysis. The further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis.
• Score: 9.385936248154987
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.

Related papers

• Granularity Matters in Long-Tail Learning [62.30734737735273]
  We offer a novel perspective on long-tail learning, inspired by an observation: datasets with finer granularity tend to be less affected by data imbalance. We introduce open-set auxiliary classes that are visually similar to existing ones, aiming to enhance representation learning for both head and tail classes. To prevent the overwhelming presence of auxiliary classes from disrupting training, we introduce a neighbor-silencing loss.
  arXiv  Detail & Related papers  (2024-10-21T13:06:21Z)
• 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)
• A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images [71.26717896083433]
  Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging.
  arXiv  Detail & Related papers  (2023-09-05T19:45:09Z)
• Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework [0.0]
  scarcity of problem-dependent training data has become a larger issue in the way of easy application of deep learning in the medical sector. Few hot learning algorithms determine to solve the data limitation problems by extracting the characteristics from a small dataset. In the medical sector, there is frequently a shortage of available datasets in respect of some confidential diseases.
  arXiv  Detail & Related papers  (2023-05-08T01:05:22Z)
• Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions [66.40971096248946]
  In this paper, we collect a series of MedISeg tricks for different model implementation phases. We experimentally explore the effectiveness of these tricks on consistent baselines. We also open-sourced a strong MedISeg repository, where each component has the advantage of plug-and-play.
  arXiv  Detail & Related papers  (2022-09-21T12:30:05Z)
• 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)
• Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications [8.69535649683089]
  We show how contrastive self-supervised learning can reduce the annotation effort within digital pathology. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications.
  arXiv  Detail & Related papers  (2021-12-10T16:08:57Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)
• 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.