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A multi-reconstruction study of breast density estimation using Deep Learning [article]

Vikash Gupta, Mutlu Demirer, Robert W. Maxwell, Richard D. White, Barbaros Selnur Erdal
2022 arXiv   pre-print
Deep-learning studies for breast density estimation use only a single modality for training a neural network. However, doing so restricts the number of images in the dataset.  ...  There have been efforts in the direction of automating a breast density classification pipeline. Breast density estimation is one of the key tasks performed during a screening exam.  ...  Most machine learning models are only as good as the training data provided. Before the proliferation of deep learning based models, several quantitative methods were used to measure breast density.  ... 
arXiv:2202.08238v2 fatcat:lif4zbg65zdtho75ia3kcqe7ie

A multi-reconstruction study of breast density estimation using Deep Learning [article]

Vikash Gupta, Mutlu Demirer, Robert W. Maxwell, Richard D. White, Barabaros Selnur Erdal
2022
Deep-learning studies for breast density estimation use only a single modality for training a neural network. However, doing so restricts the number of images in the dataset.  ...  There have been efforts in the direction of automating a breast density classification pipeline. Breast density estimation is one of the key tasks performed during a screening exam.  ...  Most machine learning models are only as good as the training data provided. Before the proliferation of deep learning based models, several quantitative methods were used to measure breast density.  ... 
doi:10.48550/arxiv.2202.08238 fatcat:2uddyqzcznc4vdqcdzq32j7r54

Front Matter: Volume 11312

Hilde Bosmans, Guang-Hong Chen
2020 Medical Imaging 2020: Physics of Medical Imaging  
breast CT 11312 0M Classification of breast calcifications in dual-energy FFDM using a convolutional neural network: simulation study BREAST IMAGING: NEW TECHNOLOGY 0O Volumetric breast density estimation  ...  for CT image reconstruction 11312 4O Evaluation of deep learning segmentation for rapid, patient-specific CT organ dose estimation using an LBTE solver 11312 4P Deep learning-based low dose CT imaging  ... 
doi:10.1117/12.2570912 fatcat:vl6kcecvhvfr5ogs3og5czzvwq

Front Matter: Volume 11595

Hilde Bosmans, Wei Zhao, Lifeng Yu
2021 Medical Imaging 2021: Physics of Medical Imaging  
The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon. Please use the following format to cite material from these proceedings:  ...  The papers reflect the work and thoughts of the authors and are published herein as submitted.  ...  28 Dual energy chest x-ray for improved COVID-19 detection using a dual-layer flat-panel detector: simulation and phantom studies 11595 29 Assessment of reproducibility of volumetric breast density measurement  ... 
doi:10.1117/12.2595450 fatcat:dji2t6dpdfantjoyxtpdik6yvu

Front Matter: Volume 10573

Guang-Hong Chen, Joseph Y. Lo, Taly Gilat Schmidt
2018 Medical Imaging 2018: Physics of Medical Imaging  
using a Base 36 numbering system employing both numerals and letters.  ...  SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  (CT) images of the lung using deep learning [10573-57] 10573 1N Deep learning angiography (DLA): three-dimensional C-arm cone beam CT angiography generated from deep learning method using a convolutional  ... 
doi:10.1117/12.2323748 fatcat:mn5csad2mjezljnvzxem3rhk5i

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

Xiao-Xia Yin, Lihua Yin, Sillas Hadjiloucas
2020 Applied Sciences  
Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward.  ...  The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI.  ...  Learning Deep learning is rapidly becoming a very useful tool for studying biomedical images.  ... 
doi:10.3390/app10207201 fatcat:tofpvyllzbautos4my26xajqfe

Volumetric breast density estimation on MRI using explainable deep learning regression

Bas H. M. van der Velden, Markus H. A. Janse, Max A. A. Ragusi, Claudette E. Loo, Kenneth G. A. Gilhuijs
2020 Scientific Reports  
A total of 615 patients with breast cancer were included for volumetric breast density estimation.  ...  A 3-dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density.  ...  We evaluated the deep learning algorithm by comparing the volumetric density estimations of the CNN to the ground truth using Spearman correlations coefficient.  ... 
doi:10.1038/s41598-020-75167-6 pmid:33093572 fatcat:akb3r7emoneenfiz626bquasu4

Front Matter: Volume 10718

Elizabeth A. Krupinski
2018 14th International Workshop on Breast Imaging (IWBI 2018)  
using a Base 36 numbering system employing both numerals and letters.  ...  A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  in breast cancer histopathological images: a preliminary study [10718-5] 10718 1F Mass detection in mammograms using pre-trained deep learning models [10718-12] 10718 1G Automatic estimation of  ... 
doi:10.1117/12.2502754 dblp:conf/iwbi/X18 fatcat:pwftmdgjcza3lnhzk4yua6npiy

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
using a Base 36 numbering system employing both numerals and letters.  ...  A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  approach for breast mass classification in mammography images [10134-90] 10134 2O A novel deep learning-based approach to high accuracy breast density estimation in digital mammography [10134-92]  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

A residual dense network assisted sparse view reconstruction for breast computed tomography

Zhiyang Fu, Hsin Wu Tseng, Srinivasan Vedantham, Andrew Karellas, Ali Bilgin
2020 Scientific Reports  
AbstractTo develop and investigate a deep learning approach that uses sparse-view acquisition in dedicated breast computed tomography for radiation dose reduction, we propose a framework that combines  ...  3D sparse-view cone-beam acquisition with a multi-slice residual dense network (MS-RDN) reconstruction.  ...  The contents are solely the responsibility of the authors and do not represent the official views of the NIH or the NCI.  ... 
doi:10.1038/s41598-020-77923-0 pmid:33273541 fatcat:nyszsj7hqvh7bg77ypuf66u2sa

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
using a Base 36 numbering system employing both numerals and letters.  ...  SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  dynamic contrast-enhanced breast MRI in the diagnosis of breast cancer: a pilot study [10575-138] 10575 3V Reduction in training time of a deep learning model in detection of lesions in CT [10575-139]  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Front Matter: Volume 11319

Nicole V. Ruiter, Brett C. Byram
2020 Medical Imaging 2020: Ultrasonic Imaging and Tomography  
using a Base 36 numbering system employing both numerals and letters.  ...  A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  11319 0F Sound speed estimation in layered media using the angular coherence of plane waves [11319-13] iii Proc. of SPIE Vol. 11319 1131901-3 Clustering based quantitative breast density assessment  ... 
doi:10.1117/12.2570236 fatcat:qfngmakgkfe5dm4njkqp4sjxne

Front Matter: Volume 12035

Claudia R. Mello-Thoms, Sian Taylor-Phillips
2022 Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment  
with activation map [12035-38] 12035 0Y AI-based analysis of radiologist's eye movements for fatigue estimation: a pilot study on chest X-rays [12035-39] 12035 0Z Comparison of deep learning architectures  ...  using radiomics and deep transfer learning: an assessment study [12035-35] 12035 0V Case-based repeatability and operating point variability of AI: breast lesion classification based on deep transfer  ... 
doi:10.1117/12.2638123 fatcat:avdabxr5rfbt3hk4emgzgr7z3i

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Aimilia Gastounioti, Shyam Desai, Vinayak S. Ahluwalia, Emily F. Conant, Despina Kontos
2022 Breast Cancer Research  
Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's  ...  With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive  ...  [36] introduced a DL method that first learned a feature hierarchy from unlabeled data and then used a classifier to estimate area percent density (APD) from raw FFDM images.  ... 
doi:10.1186/s13058-022-01509-z pmid:35184757 pmcid:PMC8859891 fatcat:n5rs4ponqva63csdsgudqzmozy

Three-dimensional stochastic numerical breast phantoms for enabling virtual imaging trials of ultrasound computed tomography

Fu Li, Umberto Villa, Seonyeong Park, Mark A. Anastasio
2021 IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control  
Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development  ...  To demonstrate the use of the phantoms in virtual USCT studies, two brief case studies are presented that address the development and assessment of image reconstruction procedures.  ...  Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.  ... 
doi:10.1109/tuffc.2021.3112544 pmid:34520354 pmcid:PMC8790767 fatcat:4nrwfmubdvcahkmhrwfl463eaq
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