Patchless multi-stage transfer learning for improved mammographic breast mass classification
Simple Summary In this study, we propose a novel deep-learning method based on multi-
stage transfer learning (MSTL) from ImageNet and cancer cell line image pre-trained models …
stage transfer learning (MSTL) from ImageNet and cancer cell line image pre-trained models …
Automatic breast density classification using a convolutional neural network architecture search procedure
Breast parenchymal density is considered a strong indicator of breast cancer risk and
therefore useful for preventive tasks. Measurement of breast density is often qualitative and …
therefore useful for preventive tasks. Measurement of breast density is often qualitative and …
End-to-end learning of fused image and non-image features for improved breast cancer classification from mri
Breast cancer diagnosis is inherently multimodal. To assess a patient's cancer status,
physicians integrate imaging findings with a variety of clinical risk factor data. Despite this …
physicians integrate imaging findings with a variety of clinical risk factor data. Despite this …
A novel multi-scale adversarial networks for precise segmentation of x-ray breast mass
J Chen, L Chen, S Wang, P Chen - IEEE Access, 2020 - ieeexplore.ieee.org
With the constant changes of people's lifestyle and living environment, the morbidity of
breast cancer is increasing year by year. It is highly imperative to develop an effective breast …
breast cancer is increasing year by year. It is highly imperative to develop an effective breast …
A multisite study of a breast density deep learning model for full-field digital mammography and synthetic mammography
TP Matthews, S Singh, B Mombourquette… - Radiology: Artificial …, 2020 - pubs.rsna.org
Purpose To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density
deep learning (DL) model in a multisite setting for synthetic two-dimensional mammographic …
deep learning (DL) model in a multisite setting for synthetic two-dimensional mammographic …
A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI
Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast
volume, increases the risk of developing breast cancer. Although previous studies have …
volume, increases the risk of developing breast cancer. Although previous studies have …
Towards improved breast mass detection using dual-view mammogram matching
Breast cancer screening benefits from the visual analysis of multiple views of routine
mammograms. As for clinical practice, computer-aided diagnosis (CAD) systems could be …
mammograms. As for clinical practice, computer-aided diagnosis (CAD) systems could be …
Multi-level nested pyramid network for mass segmentation in mammograms
Mass segmentation in mammograms is an important and challenging topic in breast cancer
computer-aided diagnosis. In this work, we propose a novel multi-level nested pyramid …
computer-aided diagnosis. In this work, we propose a novel multi-level nested pyramid …
Mass segmentation and classification from film mammograms using cascaded deep transfer learning
VM Tiryaki - Biomedical Signal Processing and Control, 2023 - Elsevier
Breast cancer is the most common type of cancer among women worldwide. Early breast
cancers have a high chance of cure so early diagnosis is critical. Mammography screening …
cancers have a high chance of cure so early diagnosis is critical. Mammography screening …
MAMMO: A deep learning solution for facilitating radiologist-machine collaboration in breast cancer diagnosis
With an aging and growing population, the number of women requiring either screening or
symptomatic mammograms is increasing. To reduce the number of mammograms that need …
symptomatic mammograms is increasing. To reduce the number of mammograms that need …