Proceedings ArticleDOI
Deep detection and classification of mitotic figures
Balamurali Murugesan,Sakthivel Selvaraj,Kaushik Sarveswaran,Keerthi Ram,Jayaraj Joseph,Mohanasankar Sivaprakasam +5 more
- Vol. 10956, pp 177-182
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TLDR
The mitotic detection task is carried out with state of the art object detector (Faster R-CNN) and classifiers (Resnet152, Densenet169, and Densenets201) for ICPR 2012 dataset and it is found that mitosis detection for scanner images is difficult.Abstract:
Breast cancer is the second largest cause of cancer death among women after skin cancer. Mitotic count is an important biomarker for predicting the breast cancer prognosis according to Nottingham Grading System. Pathologists look for tumour areas and select 10 HPF(high power field) images and assign a grade based on the number of mitotic counts. Mitosis detection is a tedious task because the pathologist has to inspect a larger area. The pathologist’s views about mitotic cell are also subjective. Because of these problems, an assisting tool for the pathologist will generalize and reduce the time for diagnosis. Due to recent advancements in whole slide imaging, CAD(computer-aided diagnosis) systems are becoming popular. Mitosis detection for scanner images is difficult because of variability in shape, color, texture and its similar appearance to apoptotic nuclei, darkly stained nuclei structures. In this paper, the mitotic detection task is carried out with state of the art object detector (Faster R-CNN) and classifiers (Resnet152, Densenet169, and Densenet201) for ICPR 2012 dataset. The Faster R-CNN is used in two ways. In first, it was treated as an object detector which gave an F1-score of 0.79 while in second, it was treated as a Region Proposal Network followed by an ensemble of classifiers giving an F1-score 0.75.read more
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Journal ArticleDOI
MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue
Sercan Cayir,Gizem Solmaz,Huseyin Kusetogullari,Fatma Tokat,Engin Bozaba,Sencer Karakaya,O. Iheme Leonardo,Eren Tekin,Cisem Yazici,Gülşah Özsoy,Samet Ayalti,Cavit Kerem Kayhan,Ümit Ince,Burak Uzel,Onur Kılıç +14 more
TL;DR: In this paper , a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer.
Proceedings ArticleDOI
Prostate Localization in 2D Sequence MR with Fusion of Center Position Prior and Sequence Correlation
TL;DR: A prostate organ localization algorithm based on the state-of-art object detection framework Faster R-CNN is proposed in this paper for Magnetic Resonance (MR) slice sequence and ResNet-50 with spatial attention mechanism is introduced as the network's feature extraction module to enhance the sensitivity of the network to the spatial location features.
References
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
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