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 …
[HTML][HTML] Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
A Gastounioti, S Desai, VS Ahluwalia, EF Conant… - Breast Cancer …, 2022 - Springer
Background Improved breast cancer risk assessment models are needed to enable
personalized screening strategies that achieve better harm-to-benefit ratio based on earlier …
personalized screening strategies that achieve better harm-to-benefit ratio based on earlier …
Attention guided neural ODE network for breast tumor segmentation in medical images
J Ru, B Lu, B Chen, J Shi, G Chen, M Wang… - Computers in Biology …, 2023 - Elsevier
Breast cancer is the most common cancer in women. Ultrasound is a widely used screening
tool for its portability and easy operation, and DCE-MRI can highlight the lesions more …
tool for its portability and easy operation, and DCE-MRI can highlight the lesions more …
[HTML][HTML] GABNet: Global attention block for retinal OCT disease classification
Introduction The retina represents a critical ocular structure. Of the various ophthalmic
afflictions, retinal pathologies have garnered considerable scientific interest, owing to their …
afflictions, retinal pathologies have garnered considerable scientific interest, owing to their …
Brain MR images segmentation using 3D CNN with features recalibration mechanism for segmented CT generation
The segmentation of MR (magnetic resonance) images is a simple approach to create
Pseudo CT images which are useful for many medical imaging analysis applications. One of …
Pseudo CT images which are useful for many medical imaging analysis applications. One of …
ResNet and its application to medical image processing: Research progress and challenges
Background and objective Deep learning, a novel approach and subset of machine
learning, has drawn a growing amount of attention from computer vision researchers in …
learning, has drawn a growing amount of attention from computer vision researchers in …
CNN-Wavelet scattering textural feature fusion for classifying breast tissue in mammograms
Visual interpretation from radiologists employs computer-aided diagnosis (CAD) to make
clinical diagnoses by analyzing breast tissue images and assessing their texture. Aside from …
clinical diagnoses by analyzing breast tissue images and assessing their texture. Aside from …
[HTML][HTML] Enhancement technique based on the breast density level for mammogram for computer-aided diagnosis
Mass detection in mammograms has a limited approach to the presence of a mass in
overlapping denser fibroglandular breast regions. In addition, various breast density levels …
overlapping denser fibroglandular breast regions. In addition, various breast density levels …
[HTML][HTML] Breast Cancer Detection in Thermography Using Convolutional Neural Networks (CNNs) with Deep Attention Mechanisms
A Alshehri, D AlSaeed - Applied Sciences, 2022 - mdpi.com
Featured Application Medical diagnosis and computer-aided diagnosis systems. Abstract
Breast cancer is one of the most common types of cancer among women. Accurate …
Breast cancer is one of the most common types of cancer among women. Accurate …
Morph_SPCNN model and its application in breast density segmentation
Breast density is known as a significant indicator of breast cancer risk prediction and greatly
reduces the digital mammograms sensitivity. In this work, based on the simple pulse coupled …
reduces the digital mammograms sensitivity. In this work, based on the simple pulse coupled …