Proceedings ArticleDOI
Survey on Mitosis Detection for Aggressive Breast Cancer from Histological Images
Hanan Hussain,Omar Hujran,K. P Nitha +2 more
- pp 232-236
TLDR
Top approaches for mitosis detection, either implemented Random Forest (RF) classifier exploiting intensity feature or used deep learning methods like Convolutional Neural Network (CNN) to give out the best results.Abstract:
The mitotic count is a relevant factor for grading invasive breast cancer. Since it is subject to human prone error, requires more time for completion and the nuclei look similar during all stages of mitosis, automatic detection of mitosis is a good solution to overcome these problems. In this paper, the top methodologies used for mitosis detection are analyzed. Some of them were a part of challenging competitions conducted worldwide. Analysis of the result shows that top approaches, either implemented Random Forest (RF) classifier exploiting intensity feature or used deep learning methods like Convolutional Neural Network (CNN) to give out the best results. It was also found that the ensemble classifiers gives better performance. A preliminary experiment conducted on cascaded RF and Artificial Neural Network (ANN) results in better accuracy than individual classifiers.read more
Citations
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Posted ContentDOI
Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization
TL;DR: This paper proposes a method for mitosis detection in histopathology images of MIDOG2022 challenge dataset based on unsupervised domain adaptation at inputlevel, and a combination of color normalization and object detection.
Journal ArticleDOI
Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization
TL;DR: In this paper , a two-step approach is proposed for mitosis detection in histopathology images in an unsupervised domain adaptation setting, where the first step is color normalization and the second step is object detection.
References
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Neural-network classifiers for recognizing totally unconstrained handwritten numerals
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