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 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 …
[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 …
Breast cancer histopathological image classification using a hybrid deep neural network
Even with the rapid advances in medical sciences, histopathological diagnosis is still
considered the gold standard in diagnosing cancer. However, the complexity of …
considered the gold standard in diagnosing cancer. However, the complexity of …
Going deep in medical image analysis: concepts, methods, challenges, and future directions
Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …
technology has recently attracted so much interest of the Medical Imaging Community that it …
A comprehensive review for breast histopathology image analysis using classical and deep neural networks
Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …
histopathological images contain sufficient phenotypic information, they play an …
Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model
Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a
reduction in the breast cancer death rate. With the help of a computer-aided diagnosis …
reduction in the breast cancer death rate. With the help of a computer-aided diagnosis …
A survey on artificial intelligence in histopathology image analysis
The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …
dramatically transformed pathologists' workflow and allowed the use of computer systems in …