Deep learning in mammography and breast histology, an overview and future trends
A Hamidinekoo, E Denton, A Rampun, K Honnor… - Medical image …, 2018 - Elsevier
Recent improvements in biomedical image analysis using deep learning based neural
networks could be exploited to enhance the performance of Computer Aided Diagnosis …
networks could be exploited to enhance the performance of Computer Aided Diagnosis …
Digital image analysis in breast pathology—from image processing techniques to artificial intelligence
Breast cancer is the most common malignant disease in women worldwide. In recent
decades, earlier diagnosis and better adjuvant therapy have substantially improved patient …
decades, earlier diagnosis and better adjuvant therapy have substantially improved patient …
Predicting cancer outcomes from histology and genomics using convolutional networks
Cancer histology reflects underlying molecular processes and disease progression and
contains rich phenotypic information that is predictive of patient outcomes. In this study, we …
contains rich phenotypic information that is predictive of patient outcomes. In this study, we …
Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent
With the increasing ability to routinely and rapidly digitize whole slide images with slide
scanners, there has been interest in developing computerized image analysis algorithms for …
scanners, there has been interest in developing computerized image analysis algorithms for …
Intelligent hybrid deep learning model for breast cancer detection
Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most
common cancers in women. Women are also at risk from BC, the second most life …
common cancers in women. Women are also at risk from BC, the second most life …
Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks
A Cruz-Roa, A Basavanhally… - Medical Imaging …, 2014 - spiedigitallibrary.org
This paper presents a deep learning approach for automatic detection and visual analysis of
invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer …
invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer …
Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland
formation within histology images. To do this, it is important to consider the overall tissue …
formation within histology images. To do this, it is important to consider the overall tissue …
Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential
Digital pathology represents one of the major evolutions in modern medicine. Pathological
examinations constitute the gold standard in many medical protocols, and also play a critical …
examinations constitute the gold standard in many medical protocols, and also play a critical …
Multiple-instance learning for medical image and video analysis
G Quellec, G Cazuguel, B Cochener… - IEEE reviews in …, 2017 - ieeexplore.ieee.org
Multiple-instance learning (MIL) is a recent machine-learning paradigm that is particularly
well suited to medical image and video analysis (MIVA) tasks. Based solely on class labels …
well suited to medical image and video analysis (MIVA) tasks. Based solely on class labels …
Breast cancer detection, segmentation and classification on histopathology images analysis: a systematic review
R Krithiga, P Geetha - Archives of Computational Methods in Engineering, 2021 - Springer
Digital pathology represents a major evolution in modern medicine. Pathological
examinations constitute the standard in medical protocols and the law, and call for specific …
examinations constitute the standard in medical protocols and the law, and call for specific …