Strategies for enhancing the multi-stage classification performances of her2 breast cancer from hematoxylin and eosin images
MSH Shovon, MJ Islam, MNAK Nabil, MM Molla… - Diagnostics, 2022 - mdpi.com
Breast cancer is a significant health concern among women. Prompt diagnosis can diminish
the mortality rate and direct patients to take steps for cancer treatment. Recently, deep …
the mortality rate and direct patients to take steps for cancer treatment. Recently, deep …
Hepatocellular carcinoma recognition from ultrasound images using combinations of conventional and deep learning techniques
Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third
cause of cancer-related deaths worldwide. For many years, the golden standard for HCC …
cause of cancer-related deaths worldwide. For many years, the golden standard for HCC …
Hepatocellular carcinoma automatic diagnosis within CEUS and B-mode ultrasound images using advanced machine learning methods
Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor, being present
in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The …
in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The …
A review on data fusion of multidimensional medical and biomedical data
Data fusion aims to provide a more accurate description of a sample than any one source of
data alone. At the same time, data fusion minimizes the uncertainty of the results by …
data alone. At the same time, data fusion minimizes the uncertainty of the results by …
Automatic segmentation of immunohistochemical images based on U-net architecture
O Berezsky, O Pitsun, B Derysh… - 2021 IEEE 16th …, 2021 - ieeexplore.ieee.org
In this paper, the application of convolutional neural networks for automatic segmentation of
immunohistochemical images based on the U-net architecture is investigated. The quality of …
immunohistochemical images based on the U-net architecture is investigated. The quality of …
Improving the performance of multi-stage HER2 breast cancer detection in hematoxylin-eosin images based on ensemble deep learning
Background: Breast cancer is the most frequently diagnosed cancer among women
worldwide, and histopathology is the gold standard in diagnosing the disease. Hematoxylin …
worldwide, and histopathology is the gold standard in diagnosing the disease. Hematoxylin …
A Novel Decision Level Class-Wise Ensemble Method in Deep Learning for Automatic Multi-Class Classification of HER2 Breast Cancer Hematoxylin-Eosin Images
The Human Epidermal Growth Factor Receptor 2 (HER2) is one of the aggressive subtypes
of breast cancer. The HER2 status decides the requirement of breast cancer patients to …
of breast cancer. The HER2 status decides the requirement of breast cancer patients to …
A Virtual Staining Method for Immunohistochemical Images of Breast Cancer
Breast cancer is a common malignant cancer. Detection of human epidermal growth factor
receptor 2 (HER2) status based on immunohistochemistry (IHC) is an effective method for …
receptor 2 (HER2) status based on immunohistochemistry (IHC) is an effective method for …
Automated HER2 scoring in breast cancer HE-stained images using hybrid deep learning ensemble and probabilistic optimization
Human epidermal growth factor receptor 2 (HER2) is a critical gene that serves as a receptor
to transmit signals for aggressive cell division in cancer cells. Hence, testing of HER2 is …
to transmit signals for aggressive cell division in cancer cells. Hence, testing of HER2 is …
Improving the Performance of Multi-Stage Her2 Breast Cancer Detection in Hematoxylin-Eosin Images Based on Ensemble Deep Learning
Hematoxylin and Eosin staining, routinely employed to observe the overall tissue structure,
is an affordable and commonly practiced cancer diagnosis. In contrast …
is an affordable and commonly practiced cancer diagnosis. In contrast …