Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
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Abstract (arXiv)
While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2018.
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Citing papers per year: 2021: 5; 2022: 3; 2023: 1.
9 papers.
- 2023Artificial Intelligence for Digital and Computational Pathologycited by 106
- 2021Hierarchical Graph Representations in Digital Pathologycited by 58
- 2021A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approachescited by 44
- 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
- 2022ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classificationcited by 21
- 2022Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) datasetcited by 15
- 2021A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformerscited by 7
- 2021Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classificationcited by 5
- 2021Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysiscited by 2
- 2023Artificial Intelligence for Digital and Computational Pathology13 Dec 2023
- 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images26 Apr 2022
- 2022ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification15 Feb 2022
- 2022Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset27 Jan 2022
- 2021Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification29 Jun 2021
- 2021A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers13 Apr 2021
- 2021Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis1 Apr 2021
- 2021Hierarchical Graph Representations in Digital Pathology22 Feb 2021
- 2021A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches21 Feb 2021