scholarly journals PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 45133-45147 ◽  
Author(s):  
Meriem Sebai ◽  
Tianjiang Wang ◽  
Saad Ali Al-Fadhli
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Learning Framework ◽  
Mitosis Detection ◽  
Histopathology Images

scholarly journals ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate

2021 ◽  
Vol 68 ◽  
pp. 101919 ◽  
Author(s):  
Wei Shao ◽  
Linda Banh ◽  
Christian A. Kunder ◽  
Richard E. Fan ◽  
Simon J.C. Soerensen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Histopathology Images

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

2020 ◽  
Vol 9 (3) ◽  
pp. 749 ◽  
Author(s):  
Tahir Mahmood ◽  
Muhammad Arsalan ◽  
Muhammad Owais ◽  
Min Beom Lee ◽  
Kang Ryoung Park
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Computational Cost ◽  
Mitotic Cell ◽  
Medical Doctor ◽  
Cell Detection ◽  
Generalization Capability ◽  
Mitosis Detection ◽  
Histopathology Images ◽  
Mitotic Cells

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.

scholarly journals Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4373 ◽  
Author(s):  
Zabit Hameed ◽  
Sofia Zahia ◽  
Begonya Garcia-Zapirain ◽  
José Javier Aguirre ◽  
Ana María Vanegas
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Diagnostic System ◽  
Microscopic Analysis ◽  
Classification Performance ◽  
Histopathological Analysis ◽  
Learning Approach ◽  
Ensemble Strategy ◽  
Histopathology Images

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.

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