Results 21 to 30 of about 67,036 (249)

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

IEEE Access, 2021
Mitotic figure count acts as a proliferative marker to measure aggressiveness of the breast cancer tumor. In this article, we have proposed a novel framework named SmallMitosis to detect mitotic cells particularly very small size mitosis from hematoxylin
Tasleem Kausar, Mingjiang Wang, M. Adnan Ashraf, Adeeba Kausar   +3 more
doaj   +1 more source

Multi-task RetinaNet for Mitosis Detection

, 2023
The account of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, it is a highly challenging task to detect mitotic cells in tumor tissues. At the same time, although advanced deep learning method have achieved great success in cell detection, the performance is often unsatisfactory when ...
Yang, Chen, Ziyue, Wang, Zijie, Fang, Hao, Bian, Yongbing, Zhang   +4 more
openaire   +2 more sources

MitosisNet: End-to-End Mitotic Cell Detection by Multi-Task Learning

IEEE Access, 2020
Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis.
Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, T.J. Bowen, Vijayan K. Asari   +4 more
doaj   +1 more source

Robust Multi-Domain Mitosis Detection

, 2021
Domain variability is a common bottle neck in developing generalisable algorithms for various medical applications. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a target representative feature space through unpaired image to image translation (CycleGAN). We comprehensively
Hussain, Mustaffa, Gangnani, Ritesh, Kadiyala, Sasidhar   +2 more
openaire   +2 more sources

PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images

IEEE Access, 2020
Detection of mitotic tumor cells per tissue area is one of the critical markers of breast cancer prognosis. The aim of this paper is to develop a method for the automatic detection of mitotic figures from breast cancer histological slides using a ...
Meriem Sebai, Tianjiang Wang, Saad Ali Al-Fadhli   +2 more
doaj   +1 more source

Representation of Differential Learning Method for Mitosis Detection [PDF]

Journal of Healthcare Engineering, 2021
The breast cancer microscopy images acquire information about the patient’s ailment, and the automated mitotic cell detection outcomes have generally been utilized to ease the massive amount of pathologist’s work and help the pathologists make clinical decisions quickly.
Haider Ali, Hansheng Li, Ephrem Afele Retta, Imran Ul Haq, Zhenzhen Guo, Xin Han, Lei Cui, Lin Yang, Jun Feng   +8 more
openaire   +1 more source

Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method. [PDF]

PLoS ONE, 2016
Tumor proliferation speed, most commonly assessed by counting of mitotic figures in histological slide preparations, is an important biomarker for breast cancer.
Mitko Veta, Paul J van Diest, Mehdi Jiwa, Shaimaa Al-Janabi, Josien P W Pluim   +4 more
doaj   +1 more source

Driving Training-Based Optimization- Multitask Fuzzy C-Means (DTBO-MFCM) Image Segmentation and Robust Deep Learning Algorithm for Multicenter Breast Histopathological Images

IEEE Access, 2023
The second most frequent disease in terms of diagnoses is breast cancer, which has had tremendous impact on women’s lives all around the world.
Afnan M. Alhassan
doaj   +1 more source

Sk-Unet Model with Fourier Domain for Mitosis Detection [PDF]

, 2022
Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem.
Yang, Sen, Luo, Feng, Zhang, Jun, Wang, Xiyue   +3 more
openaire   +2 more sources

Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection [PDF]

, 2018
Accepted for MICCAI COMPAY 2018 ...
Akram, S. U. (Saad Ullah), Qaiser, T. (Talha), Graham, S. (Simon), Kannala, J. (Juho), Heikkilä, J. (Janne), Rajpoot, N. (Nasir)   +5 more
openaire   +4 more sources