Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

Abstract

Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important biomarker for predicting the aggressiveness, prognosis, and grade of breast cancer. In general, pathologists manually examine histopathology images under high-resolution microscopes for the detection of mitotic cells. However, because of the minute differences between the mitotic and normal cells, this process is tiresome, time-consuming, and subjective. To overcome these challenges, artificial-intelligence-based (AI-based) techniques have been developed which automatically detect mitotic cells in the histopathology images. Such AI techniques accelerate the diagnosis and can be used as a second-opinion system for a medical doctor. Previously, conventional image-processing techniques were used for the detection of mitotic cells, which have low accuracy and high computational cost. Therefore, a number of deep-learning techniques that demonstrate outstanding performance and low computational cost were recently developed; however, they still require improvement in terms of accuracy and reliability. Therefore, we present a multistage mitotic-cell-detection method based on Faster region convolutional neural network (Faster R-CNN) and deep CNNs. Two open datasets (international conference on pattern recognition (ICPR) 2012 and ICPR 2014 (MITOS-ATYPIA-14)) of breast cancer histopathology were used in our experiments. The experimental results showed that our method achieves the state-of-the-art results of 0.876 precision, 0.841 recall, and 0.858 F1-measure for the ICPR 2012 dataset, and 0.848 precision, 0.583 recall, and 0.691 F1-measure for the ICPR 2014 dataset, which were higher than those obtained using previous methods. Moreover, we tested the generalization capability of our technique by testing on the tumor proliferation assessment challenge 2016 (TUPAC16) dataset and found that our technique also performs well in a cross-dataset experiment which proved the generalization capability of our proposed technique.

<p>Progress of Artificial Intelligence in Gynecological Malignant Tumors</p>

“…DL and Radiomics are creating a paradigm shift in radiology and precision medicine by developing a new area of research to be used for precision medicine. 12 For example, AI can be useful in the detection of lung nodules from CT images, [13][14][15] prediction of complete response after neoadjuvant chemoradiation for locally advanced rectal cancer, 16 prediction of the response to individual induction chemotherapy in advanced nasopharyngeal carcinoma, 17 as well as the detection of cell mitosis, 18 and lymph node metastasis 19 in breast cancer from pathological images, etc. In recent years, with the further penetration of AI into the field of medical treatment, gynecologic oncologists are unwilling to lag behind.…”

Artificial intelligence in COVID-19 ultrastructure

“…All of these findings are easily amenable to analysis by computer vision, a subset of AI. To name a few examples, computer vision has been used to detect and count lymphocytes and mitotic figures in breast cancer,[ 7 ] evaluate nuclear atypia,[ 8 ] detect lymph node metastasis,[ 9 ] diagnose and grade prostate cancer,[ 10 ] as well as quantify immunohistochemistry biomarkers. [ 11 ]…”

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

“…In [8] Region of Interest ROIs are extracted by applying segmentation and detection modules while the mitosis and non-mitosis regions are categorized by classification model. Mahmood et al [9] present a multistage mitotic-cell detection method. In first stage, Faster R-CNN detector is used for primary detection of mitotic cells.…”