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

Abstract

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.

22 pages, 38 figures. arXiv admin note: substantial text overlap with arXiv:2102.10553

References in corpus (15)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A Survey on Visual Transformer
• Assessment of algorithms for mitosis detection in breast cancer histopathology images
• A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
• RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
• CAGNet: Content-Aware Guidance for Salient Object Detection
• Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture
• Gastric histopathology image segmentation using a hierarchical conditional random field
• An attention-based multi-resolution model for prostate whole slide imageclassification and localization
• MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
• Automatic labeling of molecular biomarkers of whole slide immunohistochemistry images using fully convolutional networks
• Feature based Sequential Classifier with Attention Mechanism
• A deep learning based multiscale approach to segment cancer area in liver whole slide image