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Mitosis

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Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein.

BMC bioinformatics
BACKGROUND: Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional e...

Impact of chromogranin A, differentiation, and mitoses in nonfunctional pancreatic neuroendocrine tumors ≤ 2 cm.

The Journal of surgical research
BACKGROUND: Small pancreatic neuroendocrine tumors (PNETs) are a unique subset of pancreatic neoplasms. Chromogranin A (CgA) levels, mitotic rate, and histologic differentiation are often used to characterize PNET behavior. This study evaluates the i...

Detection of mitotic nuclei in breast histopathology images using localized ACM and Random Kitchen Sink based classifier.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The exact measure of mitotic nuclei is a crucial parameter in breast cancer grading and prognosis. This can be achieved by improving the mitotic detection accuracy by careful design of segmentation and classification techniques. In this paper, segmen...

A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distingu...

Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection.

Computers in biology and medicine
Different types of breast cancer are affecting lives of women across the world. Common types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). While...

Reconstructing cell cycle and disease progression using deep learning.

Nature communications
We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progressi...

Efficient deep learning model for mitosis detection using breast histopathology images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mitosis detection is one of the critical factors of cancer prognosis, carrying significant diagnostic information required for breast cancer grading. It provides vital clues to estimate the aggressiveness and the proliferation rate of the tumour. The...

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Medical image analysis
Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate...

Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches.

Computers in biology and medicine
In medical practice, the mitotic cell count from histological images acts as a proliferative marker for cancer diagnosis. Therefore, an accurate method for detecting mitotic cells in histological images is essential for cancer screening. Manual evalu...

Transfer learning based deep CNN for segmentation and detection of mitoses in breast cancer histopathological images.

Microscopy (Oxford, England)
Segmentation and detection of mitotic nuclei is a challenging task. To address this problem, a Transfer Learning based fast and accurate system is proposed. To give the classifier a balanced dataset, this work exploits the concept of Transfer Learnin...