[HTML][HTML] Deep learning in histopathology: the path to the clinic
Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
EH Houssein, MM Emam, AA Ali… - Expert Systems with …, 2021 - Elsevier
Breast cancer is the second leading cause of death for women, so accurate early detection
can help decrease breast cancer mortality rates. Computer-aided detection allows …
can help decrease breast cancer mortality rates. Computer-aided detection allows …
Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning
We address the challenging problem of whole slide image (WSI) classification. WSIs have
very high resolutions and usually lack localized annotations. WSI classification can be cast …
very high resolutions and usually lack localized annotations. WSI classification can be cast …
Automated detection of COVID-19 cases using deep neural networks with X-ray images
T Ozturk, M Talo, EA Yildirim, UB Baloglu… - Computers in biology …, 2020 - Elsevier
Abstract The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of
China in December 2019, spread rapidly around the world and became a pandemic. It has …
China in December 2019, spread rapidly around the world and became a pandemic. It has …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative
analysis of imaging data for biological and biomedical applications. Many bioimage analysis …
analysis of imaging data for biological and biomedical applications. Many bioimage analysis …
[HTML][HTML] The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
A novel deep learning based framework for the detection and classification of breast cancer using transfer learning
Breast cancer is among the leading cause of mortality among women in developing as well
as under-developing countries. The detection and classification of breast cancer in the early …
as under-developing countries. The detection and classification of breast cancer in the early …
Bach: Grand challenge on breast cancer histology images
Breast cancer is the most common invasive cancer in women, affecting more than 10% of
women worldwide. Microscopic analysis of a biopsy remains one of the most important …
women worldwide. Microscopic analysis of a biopsy remains one of the most important …
Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images
Advances in artificial intelligence technologies have made it possible to obtain more
accurate and reliable results using digital images. Due to the advances in digital …
accurate and reliable results using digital images. Due to the advances in digital …