Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …
Deep learning with radiomics for disease diagnosis and treatment: challenges and potential
The high-throughput extraction of quantitative imaging features from medical images for the
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly develo** and …
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly develo** and …
Convolutional neural networks for breast cancer detection in mammography: A survey
Despite its proven record as a breast cancer screening tool, mammography remains labor-
intensive and has recognized limitations, including low sensitivity in women with dense …
intensive and has recognized limitations, including low sensitivity in women with dense …
A comprehensive survey on deep-learning-based breast cancer diagnosis
Simple Summary Breast cancer was diagnosed in 2.3 million women, and around 685,000
deaths from breast cancer were recorded globally in 2020, making it the most common …
deaths from breast cancer were recorded globally in 2020, making it the most common …
Multi-modal retinal image classification with modality-specific attention network
Recently, automatic diagnostic approaches have been widely used to classify ocular
diseases. Most of these approaches are based on a single imaging modality (eg, fundus …
diseases. Most of these approaches are based on a single imaging modality (eg, fundus …
A systematic survey of deep learning in breast cancer
In recent years, we witnessed a speeding development of deep learning in computer vision
fields like categorization, detection, and semantic segmentation. Within several years after …
fields like categorization, detection, and semantic segmentation. Within several years after …
Blockchain for privacy preserving and trustworthy distributed machine learning in multicentric medical imaging (C-DistriM)
The utility of Artificial Intelligence (AI) in healthcare strongly depends upon the quality of the
data used to build models, and the confidence in the predictions they generate. Access to …
data used to build models, and the confidence in the predictions they generate. Access to …
Development of an artificial intelligence-based breast cancer detection model by combining mammograms and medical health records
Background: Artificial intelligence (AI)-based computational models that analyze breast
cancer have been developed for decades. The present study was implemented to …
cancer have been developed for decades. The present study was implemented to …
Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic …
T Fujioka, Y Yashima, J Oyama, M Mori… - Magnetic Resonance …, 2021 - Elsevier
Purpose We aimed to evaluate deep learning approach with convolutional neural networks
(CNNs) to discriminate between benign and malignant lesions on maximum intensity …
(CNNs) to discriminate between benign and malignant lesions on maximum intensity …
Explainable multi-module semantic guided attention based network for medical image segmentation
Automated segmentation of medical images is crucial for disease diagnosis and treatment
planning. Medical image segmentation has been improved based on the convolutional …
planning. Medical image segmentation has been improved based on the convolutional …