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 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 …
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 …
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 …
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 …
[HTML][HTML] A comprehensive survey on deep-learning-based breast cancer diagnosis
Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is
gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if …
gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if …
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 …
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 …
Multi-view learning for lymph node metastasis prediction using tumor and nodal radiomics in gastric cancer
J Yang, L Wang, J Qin, J Du, M Ding… - Physics in Medicine & …, 2022 - iopscience.iop.org
Purpose. This study aims to develop and validate a multi-view learning method by the
combination of primary tumor radiomics and lymph node (LN) radiomics for the preoperative …
combination of primary tumor radiomics and lymph node (LN) radiomics for the preoperative …
Multi-scale attention-guided network for mammograms classification
For the breast mass segmentation in whole mammograms, in our studies, we observe that
there is an enormous performance reduction in the case of considering the normal data …
there is an enormous performance reduction in the case of considering the normal data …