Deep YOLO-Based Detection of Breast Cancer Mitotic-Cells in Histopathological Images

被引：3

作者：

Al Zorgani, Maisun Mohamed ^{[1
]}

Mehmood, Irfan ^{[1
]}

Ugail, Hassan ^{[1
]}

机构：

[1] Univ Bradford, Sch Media Design & Tech, Fac Engn & Informat, Bradford, W Yorkshire, England

来源：

PROCEEDINGS OF 2021 INTERNATIONAL CONFERENCE ON MEDICAL IMAGING AND COMPUTER-AIDED DIAGNOSIS, MICAD 2021 | 2022年 / 784卷

关键词：

Breast cancer histopathological images; Mitotic cell counting; Deep learning techniques; YOLO-v2; network; MITOSIS DETECTION;

D O I：

10.1007/978-981-16-3880-0_35

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitoticcells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.

引用

页码：335 / 342

页数：8

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