A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers

• URL: http://arxiv.org/abs/2104.06243v1
• Date: Tue, 13 Apr 2021 14:39:33 GMT
• Title: A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers
• Authors: Chen Li, Xintong Li, Xiaoyan Li, Md Mamunur Rahaman, Xiaoqi Li, Jian Wu, Yudong Yao, Marcin Grzegorzek
• Abstract summary: Whole slide image (WSI) has gradually played a crucial aspect in the diagnosis and analysis of diseases. To increase the objectivity and accuracy of pathologists' work, artificial neural network (ANN) methods have been generally needed.
• Score: 18.031804027273292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, with the advancement of computer-aided diagnosis (CAD) technology and whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the diagnosis and analysis of diseases. To increase the objectivity and accuracy of pathologists' work, artificial neural network (ANN) methods have been generally needed in the segmentation, classification, and detection of histopathological WSI. In this paper, WSI analysis methods based on ANN are reviewed. Firstly, the development status of WSI and ANN methods is introduced. Secondly, we summarize the common ANN methods. Next, we discuss publicly available WSI datasets and evaluation metrics. These ANN architectures for WSI processing are divided into classical neural networks and deep neural networks (DNNs) and then analyzed. Finally, the application prospect of the analytical method in this field is discussed. The important potential method is Visual Transformers.

Related papers

• Graph Neural Networks in Histopathology: Emerging Trends and Future Directions [0.0]
  The application of Graph Neural Networks in histopathology has experienced rapid growth. We identify four emerging trends: Hierarchical GNNs, Adaptive Graph Structure Learning, Multimodal GNNs, and Higher-order GNNs. Based on our findings, we propose future directions to propel the field forward.
  arXiv  Detail & Related papers  (2024-06-18T17:23:50Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey [35.29334376503123]
  This survey provides a comprehensive exploration of applications of Topological Data Analysis (TDA) within neural network analysis. We discuss different strategies to obtain topological information from data and neural networks by means of TDA. We explore practical implications of deep learning, specifically focusing on areas like adversarial detection and model selection.
  arXiv  Detail & Related papers  (2023-12-10T09:50:57Z)
• A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery [0.0]
  Review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adrial Networks (GANs) and transformer models. Common pre-processing techniques for ensuring optimal data preparation are also covered.
  arXiv  Detail & Related papers  (2023-08-18T00:40:42Z)
• Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
  Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years. NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
  arXiv  Detail & Related papers  (2022-10-28T04:38:10Z)
• A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches [21.317219960860267]
  Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. This paper reviews the methods of WSI analysis based on machine learning.
  arXiv  Detail & Related papers  (2021-02-21T08:30:48Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis [53.106414896248246]
  We present a framework that allows analysts to effectively build the solution sub-graph space and guide the network search by injecting their domain knowledge. Applying this technique in an iterative manner allows analysts to converge to the best performing neural network architecture for a given application.
  arXiv  Detail & Related papers  (2020-09-28T01:48:45Z)
• SOCRATES: Towards a Unified Platform for Neural Network Analysis [7.318255652722096]
  We aim to build a unified framework for developing techniques to analyze neural networks. We develop a platform called SOCRATES which supports a standardized format for a variety of neural network models. Experiment results show that our platform can handle a wide range of networks models and properties.
  arXiv  Detail & Related papers  (2020-07-22T05:18:57Z)
• Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks [54.27962244835622]
  This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs) In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
  arXiv  Detail & Related papers  (2020-07-03T01:37:16Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)

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.