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A Deep Framework for Cell Mitosis Detection in Microscopy Images
2020 16th International Conference on Computational Intelligence and Security (CIS), 2020Detection 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
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CTMC: Cell Tracking with Mitosis Detection Dataset Challenge
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020While significant developments have been made in cell tracking algorithms, current datasets are still limited in size and diversity, especially for data-hungry generalized deep learning models. We introduce a new larger and more diverse cell tracking dataset in terms of number of sequences, length of sequences, and cell lines, accompanied with a public
Samreen Anjum, Danna Gurari
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Symmetry-based mitosis detection in time-lapse microscopy
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015Providing a general framework for mitosis detection is challenging. The variability of the visual traits and temporal features which classify the event of cell division is huge due to the numerous cell types, perturbations, imaging techniques and protocols used in microscopy imaging analysis studies.
Topaz Gilad +3 more
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Deep Fully Convolutional Networks for Mitosis Detection
Proceedings of the 2019 4th International Conference on Robotics, Control and Automation, 2019Image recognition plays a vital role in the medical image analysis field, which depends on different medical image analysis algorithms with input data, features, parameters, and type of learning. Three crucial morphological features on Hematoxylin and Eosin 1991 (HE our model is ResNet18 pre-trained to classify with localized based on the Tensorflow ...
Mohammed Abdulkareem +3 more
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Survey of Mitosis Detection Techniques in Breast Cancer
2018 3rd International Conference on Inventive Computation Technologies (ICICT), 2018Breast Cancer is one of the most occurring cancer in women's among different types of cancer. Detection of mitosis is a challenging work in breast histopathology images and in mammography. Due to different stages of mitosis, non uniform stain illumination, size and shape variation of cells detection of mitosis is difficult.
Vinayak N. Malavade +2 more
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ACNet: Aggregated Channels Network for Automated Mitosis Detection
2019Mitosis count is a critical predictor for invasive breast cancer grading using the Nottingham grading system. Nowadays mitotic count is mainly performed on high-power fields by pathologists manually under a microscope which is a highly tedious, time-consuming and subjective task. Therefore, it is necessary to develop automated mitosis detection methods
Kaili Cheng +5 more
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Mitosis detection in breast cancer histological images with mathematical morphology
2013 21st Signal Processing and Communications Applications Conference (SIU), 2013One of the most important outcome predictors of malignant tumors is the mitotic count, i.e. the division speed of cells. This value is computed from the patient's tissue samples by medical experts, that count each mitosis case one by one under a microscope, and as such it is a time consuming process.
Erchan Aptoula +2 more
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Efficient Mitosis Detection in Breast Cancer Histology Images by RCNN
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Mitotic cell detection and counting per tissue area is an important aggressiveness indicator for the invasive breast cancer. However, manual mitosis counting by pathologists is extremely labor-intensive. Several automatic mitosis detection methods have been proposed in recent years.
De Cai +4 more
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Cassini ovals for robust mitosis detection in cellular imaging
Journal of MicroscopyAbstractAccurate detection of mitosis is crucial in automated cell analysis, yet many existing methods depend heavily on deep learning models or complex detection techniques, which can be computationally intensive and error‐prone, particularly when segmentation is incomplete.
Reza Yazdi, Hassan Khotanlou
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Mitosis Detection in Breast Cancer Using Superpixels and Ensemble Classifiers
2017Determining the severity and potential aggressiveness of breast cancer is an important step in the determination of the treatment options for a patient. Mitosis activity is one of the main components in breast cancer severity grading. Currently, mitosis counting is a laborious, prone to processing errors, done manually by a pathologist.
César Antonio Ortiz Toro +4 more
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