@inproceedings{8d90dce6f20c4a35aec71d1c876abf95,
title = "Combing colour detection and neural networks for gland detection",
abstract = "Glands are objects of interest which can be used for quantitatively analysis of histology images. Detecting glands from H&E staining histological images based on neural networks, may suffer stain variation problem. In this paper, we present a new method which combines a statistical colour detection model and a neural network to cover this problem. Colours shown at glands boundaries are pre-detected and enhanced in a pre-processing step. Then a neural network model based on Faster R-CNN is learned from these colour pixels to detect glands. This method has been tested on a Colon Histology Images Challenge Contest (GlaS) held at MICCAI 2015. The experimental results have shown the proposed method is superior to either Faster R-CNN or U-net without colour detection pre-processing. In addition, this proposed method can achieve Fl-score rank 8 in detecting benign glands and rank 5 in detecting malignant glands.",
keywords = "Colour detection, Faster R-CNN, Gland, Object detection",
author = "Jie Shu and Jiang Lei and Qiyang Gao and Qian Zhang",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 2nd International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2019 ; Conference date: 16-08-2019 Through 18-08-2019",
year = "2019",
month = aug,
day = "16",
doi = "10.1145/3357254.3357280",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "33--36",
booktitle = "2019 2nd International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2019",
}