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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), 2019
Breast 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
openaire   +2 more sources

Automated Detection of Mitosis in Embryonic Tissues

Fourth Canadian Conference on Computer and Robot Vision (CRV '07), 2007
Characterization 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
openaire   +1 more source

Enhanced Random Forest for Mitosis Detection

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing, 2014
Histopathological 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
openaire   +1 more source

Wide residual networks for mitosis detection

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017
One 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
openaire   +1 more source

Mask-Driven Mitosis Detection In Histopathology Images

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019
Mitotic 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
openaire   +1 more source

Automated mitosis detection with deep regression networks

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
Mitosis 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
openaire   +1 more source

A Deep Framework for Cell Mitosis Detection in Microscopy Images

2020 16th International Conference on Computational Intelligence and Security (CIS), 2020
Detection and tracking of multiple cells is critical in biomedical research and computer vision. Resolving lineage relationships between mitotic cells has been of fundamental interest in this filed recently. Microscopy images with cells at poor imagining conditions are difficult to detect and manual operation still remains standard procedure.
Jian Shi   +4 more
openaire   +1 more source

Mitosis detection on histopathological images using statistical detection algorithms

2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015
In 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
openaire   +1 more source

Mitosis Detection Using Image Segmentation and Object Detection

2019 IEEE Conference on Information and Communication Technology, 2019
The 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
openaire   +1 more source

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