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Mitosis Detection in the Wild Using Detection Transformers
bioRxivBackground Identification of mitotic cells and its down-stream analysis, is an important parameter in understanding the pathology of cancer, predicting response to chemotherapy and overall survival. However, their reliable detection remains challenging due to morphological overlap with other cellular structures, resulting
Vidushi Walia +6 more
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Quadra Sense: A Fusion of Deep Learning Classifiers for Mitosis Detection in Breast Cancer Histopathology [PDF]
DiagnosticsBackground/Objectives: The difficulties caused by breast cancer have been addressed in a number of ways. Since it is said to be the second most common cause of death from cancer among women, early intervention is crucial.
Afnan M. Alhassan, Nouf I. Altmami
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IEEE AccessAt present, mitosis detection in breast histopathology images is a critical issue for breast cancer grading. Due to the breast tissue having a complex structure, and mitosis and non-mitosis cells being similar to each other, traditional methods for ...
Sarah Ayashm +3 more
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MitoDet: Simple and Robust Mitosis Detection [PDF]
, 2022 Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions. While automated methods often achieve acceptable results under laboratory conditions, they frequently fail in the clinical deployment phase. This problem can be mainly attributed to a phenomenon called domain shift. An important source
Jakob Dexl +4 more
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Virtual Staining for Mitosis Detection in Breast Histopathology [PDF]
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020 We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine ...
Mercan, C. +6 more
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Contextual Prior Constrained Deep Networks for Mitosis Detection With Point Annotations
IEEE Access, 2021 We study the problem of training an accurate deep learning mitosis detection model with only point annotations. To address this challenging label-efficient deep learning problem, we propose a novel contextual prior constraint mechanism and spatial area ...
Jiangxiao Han, Xinggang Wang, Wenyu Liu
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A Heteromorphous Deep CNN Framework for Medical Image Segmentation Using Local Binary Pattern
IEEE Access, 2022 Estimating mitotic nuclei in breast cancer samples can aid in determining the tumor’s aggressiveness and grading system. Because of their strong resemblance to non-mitotic nuclei and heteromorphic form, automated evaluation of mitotic nuclei is ...
Saeed Iqbal, Adnan N. Qureshi
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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 +3 more
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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 +4 more
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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 +4 more
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