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Cell mitosis detection using deep neural networks [PDF]
Knowledge-Based Systems, 2017 Quantitative analysis of cell mitosis, the process by which cells regenerate, is important in cell biology. Automatic cell mitosis detection can greatly facilitate the investigation of cell life cycle. However, cell-type diversity, cell non-rigid deformation and high cell density pose difficulties on handcrafting visual features for traditional ...
Hua Mao, Zhang Yi
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Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures
Medical Image AnalysisCounting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists.
Mostafa Jahanifar +2 more
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An Improved Object Detection Method for Mitosis Detection
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019Breast cancer grading is important for patient prognosis, and the mitosis count is one of the most important indicators for breast cancer grading. Traditional methods use handcraft features and deep learning based methods to detect mitosis in a classified model. These methods are time-consuming and difficult for practical clinical practice application.
Haijun Lei +4 more
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Enhanced Random Forest for Mitosis Detection
Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing, 2014Histopathological grading of cancer is a measure of the cell appearance in malignant neoplasms. Grading offers an insight to the growth of the cancer and helps in developing individual treatment plans. The Nottingham grading system [12], well known method for invasive breast cancer grading, primarily relies on the mitosis count in histopathological ...
Angshuman Paul, Dipti Prasad Mukherjee
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Automated Detection of Mitosis in Embryonic Tissues
Fourth Canadian Conference on Computer and Robot Vision (CRV '07), 2007Characterization of mitosis is important for understanding the mechanisms of development in early stage embryos. In studies of cancer, another situation in which mitosis is of interest, the tissue is stained with contrast agents before mitosis characterization; an intervention that could lead to atypical development in live embryos.
Parthipan Siva +2 more
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Wide residual networks for mitosis detection
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017One of the most important prognostic markers to assess proliferation activity of breast tumors is estimating the number of mitotic figures in H&E stained tissue. We propose the use of a recently published convolutional neural network architecture, Wide Residual Networks, for mitosis detection in breast histology images. The model is trained to classify
Erwan Zerhouni +3 more
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Mask-Driven Mitosis Detection In Histopathology Images
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Mitotic detection and counting are the primary diagnostic factors used for cancer detection and grading. In this paper, we introduce a method of automatically obtaining masks for the cells and using the generated masks for mitotic detection. In the first stage of processing, we use the Mask R-CNN network to obtain the masks for the cells and also ...
Veena Dodballapur +5 more
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Automated mitosis detection with deep regression networks
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016Mitosis counting is one of the strongest prognostic markers for invasive breast cancer diagnosis. Clinical visual examination on histology slides by pathologists is tedious, error-prone and time-consuming. Furthermore, with the advent of whole slide imaging for high-throughput digitization, a large quantity of histology images need to be analyzed ...
Hao Chen 0011 +2 more
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Mitosis detection on histopathological images using statistical detection algorithms
2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015In this work, the utility and accuracy of the statistical detection algorithms for the detection of mitosis on histopathological images have been investigated. In the first stage, the subset images involving mitotic cells from the original images have been created. The occurance based texture filters have been applied to each subset image.
Mustafa Ustuner, Gokhan Bilgin
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Mitosis Detection Using Image Segmentation and Object Detection
2019 IEEE Conference on Information and Communication Technology, 2019The World Health Organisation(WHO) identifies that in women, the second most cancer deaths are caused by Breast cancer[1]. This paper presents various approaches for Mitosis detection on publicly available MITOS data set and DSB (Data Science Bowl). The process involves using a U-Net architecture consisting of convolution and deconvolution layers to ...
Nairit Banerjee +6 more
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