Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential

• URL: http://arxiv.org/abs/2206.04425v2
• Date: Wed, 6 Dec 2023 15:20:41 GMT
• Title: Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential
• Authors: Michael Gadermayr, Maximilian Tschuchnig
• Abstract summary: Digital whole slides images contain an enormous amount of information. Deep neural networks show high potential with respect to various tasks in the field of digital pathology. Deep learning algorithms require (manual) annotations in addition to the large amounts of image data to enable effective training. Multiple instance learning exhibits a powerful tool for learning deep neural networks in a scenario without fully annotated data.
• Score: 0.29008108937701327
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of digital pathology. However, a limitation is given by the fact that typical deep learning algorithms require (manual) annotations in addition to the large amounts of image data, to enable effective training. Multiple instance learning exhibits a powerful tool for learning deep neural networks in a scenario without fully annotated data. These methods are particularly effective in this domain, due to the fact that labels for a complete whole slide image are often captured routinely, whereas labels for patches, regions or pixels are not. This potential already resulted in a considerable number of publications, with the majority published in the last three years. Besides the availability of data and a high motivation from the medical perspective, the availability of powerful graphics processing units exhibits an accelerator in this field. In this paper, we provide an overview of widely and effectively used concepts of used deep multiple instance learning approaches, recent advances and also critically discuss remaining challenges and future potential.

Related papers

• Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review [0.0]
  This paper explores AI-assistive deep learning image annotation systems that provide textual suggestions, captions, or descriptions of the input image to the annotator. We review various datasets and how they contribute to the training and evaluation of AI-assistive annotation systems. Despite the promising potential, there is limited publicly available work on AI-assistive image annotation with textual output capabilities.
  arXiv  Detail & Related papers  (2024-06-28T22:56:17Z)
• HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration [1.5291251918989404]
  Digital pathology has become a standard in the pathology workflow due to its many benefits. Recent advances in deep learning-based methods for image analysis make them of potential aid in digital pathology. This paper proposes a web service that efficiently provides a tool to visualize and annotate digitized histological images.
  arXiv  Detail & Related papers  (2023-07-11T10:41:09Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• 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)
• Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
  We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives. The proposed framework can be applied in several domains in humanities and social sciences.
  arXiv  Detail & Related papers  (2022-04-05T12:19:24Z)
• Leveraging Self-Supervision for Cross-Domain Crowd Counting [71.75102529797549]
  State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. We train our network to recognize upside-down real images from regular ones and incorporate into it the ability to predict its own uncertainty. This yields an algorithm that consistently outperforms state-of-the-art cross-domain crowd counting ones without any extra computation at inference time.
  arXiv  Detail & Related papers  (2021-03-30T12:37:55Z)
• 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)
• Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios [7.41157183358269]
  We will review advances in the community that leverage deep learning for robust hyperspectral image analysis. challenges include limited ground truth and high dimensional nature of the data. Specifically, we will review unsupervised, semi-supervised and active learning approaches to image analysis.
  arXiv  Detail & Related papers  (2020-07-16T19:51:02Z)
• Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential [1.8907108368038215]
  We focus on a powerful class of architectures, called Generative Adversarial Networks (GANs), applied to histological image data. GANs enable application scenarios in this field, which were previously intractable. We present the main applications of GANs and give an outlook of some chosen promising approaches and their possible future applications.
  arXiv  Detail & Related papers  (2020-04-30T16:38:06Z)
• 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)

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.