116 Hits in 6.8 sec

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  ...  inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of  ...  Deep-LIBRA demonstrated a mean Dice score of DSC = 92.5% for breast segmentation and a mean APD difference of 4.6% with respect to "gold-standard" human-rated Cumulus APD values.  ... 
doi:10.1186/s13058-022-01509-z pmid:35184757 pmcid:PMC8859891 fatcat:n5rs4ponqva63csdsgudqzmozy

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

Morteza Heidari, Abolfazl Zargari Khuzani, Alan B Hollingsworth, Gopichandh Danala, Seyedehnafiseh Mirniaharikandehei, Yuchen Qiu, Hong Liu, Bin Zheng
2018 Physics in Medicine and Biology  
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine  ...  First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic  ...  The authors would also like to acknowledge the support from the Peggy and Charles Stephenson Cancer Center, University of Oklahoma, USA.  ... 
doi:10.1088/1361-6560/aaa1ca pmid:29239858 pmcid:PMC5801007 fatcat:fnocoi634zgpxaeea3rbxyq5qy

Deep learning in mammography and breast histology, an overview and future trends

Azam Hamidinekoo, Erika Denton, Andrik Rampun, Kate Honnor, Reyer Zwiggelaar
2018 Medical Image Analysis  
Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based  ...  Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems.  ...  Deep Learning in Mammographic Image Processing Problem statement Mammograms reflect density variations in breast tissue composition due to different X-ray attenuation in breast tissue.  ... 
doi:10.1016/ pmid:29679847 fatcat:nkrmtohwfvdtfpo3rbdvvotu2a

Fully Automated Breast Density Segmentation and Classification Using Deep Learning

Nasibeh Saffari, Hatem A. Rashwan, Mohamed Abdel-Nasser, Vivek Kumar Singh, Meritxell Arenas, Eleni Mangina, Blas Herrera, Domenec Puig
2020 Diagnostics  
This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques.  ...  The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly.  ...  Also, this work is partly supported by the Ministry of Science and Innovation (Spain) through project PID2019-105789RB-I00. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/diagnostics10110988 pmid:33238512 fatcat:gx4ozlqgunenlnctxduu6zbjze

Prediction of reader estimates of mammographic density using convolutional neural networks

Georgia V. Ionescu, Martin Fergie, Michael Berks, Elaine F. Harkness, Johan Hulleman, Adam R. Brentnall, Jack Cuzick, D. Gareth Evans, Susan M. Astley
2019 Journal of Medical Imaging  
Mammographic density is an important risk factor for breast cancer.  ...  Each CNN learns a mapping between mammographic appearance and VAS score so that at test time, they can predict VAS score for an unseen image.  ...  for breast cancer risk assessment." 47  ... 
doi:10.1117/1.jmi.6.3.031405 pmid:30746393 pmcid:PMC6357906 fatcat:boo3x5tbijbq3kea5krpwcxjuy

A novel deep learning architecture outperforming 'off‑the‑shelf' transfer learning and feature‑based methods in the automated assessment of mammographic breast density

Eleftherios Trivizakis, Georgios Ioannidis, Vasileios Melissianos, Georgios Papadakis, Aristidis Tsatsakis, Demetrios Spandidos, Kostas Marias
2019 Oncology Reports  
In this study, we propose and evaluate advanced machine learning methodologies aiming at an objective and reliable method for breast density scoring from routine mammographic images.  ...  Furthermore, differentiating breast tissue type enables patient pre‑screening stratification and risk assessment.  ...  Kallenberg et al (11) proposed a merged unsupervised segmentation and feature extraction process with an A novel deep learning architecture outperforming 'off-the-shelf' transfer learning and feature-based  ... 
doi:10.3892/or.2019.7312 pmid:31545461 pmcid:PMC6787954 fatcat:i53dwoxghnhz3dllepmb2spy2q

Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches

João Mendes, Nuno Matela
2021 Journal of Imaging  
This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk.  ...  The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in  ...  [41] aimed to use unsupervised deep learning to perform breast density segmentation and mammographic risk scoring.  ... 
doi:10.3390/jimaging7060098 fatcat:q3vxbpkdfzeflngwbptp4n3xn4

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Parita Oza, Paawan Sharma, Samir Patel, Alessandro Bruno
2021 Journal of Imaging  
mass morphological features, mammography accuracy changes with the density of the breast.  ...  Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer.  ...  The main objective of this model is to segment breast density and obtain a risk score by acquiring features from unlabeled data.  ... 
doi:10.3390/jimaging7090190 pmid:34564116 pmcid:PMC8466003 fatcat:2r2va44qe5hzhmc6pfysuzphlu

Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3+

Kuochen Zhou, Wei Li, Dazhe Zhao
2022 Technology and Health Care  
OBJECTIVE: The proposed method aims to extract breast region accurately from mammographic images where noise is suppressed, contrast is enhanced and pectoral muscle region is removed.  ...  METHODS: This paper presents a new deep learning-based breast region extraction method that combines pre-processing methods containing noise suppression using median filter, contrast enhancement using  ...  The development of deep learning and its role in medical image segmentation especially breast mammographic image will be introduced in Section 2.1.2, as well as some researches applying deep learning models  ... 
doi:10.3233/thc-228017 pmid:35124595 pmcid:PMC9028646 fatcat:gaekxdmfpzcslm5f7loaj6qwle

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

Francisco Javier Perez-Benito, Francois Signol, Juan-Carlos Perez-Cortes, Alejandro Fuster-Baggetto, Marina Pollan, Beatriz Perez-Gomez, Dolores Salas-Trejo, Maria Casals, Inmaculada Martinez, Rafael LLobet
2020 Computer Methods and Programs in Biomedicine  
Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from  ...  An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists.  ...  Acknowledgements The authors of this work like to thank to Guillermo García Colomina, Carlos Barata Ferrando and Empar Giner Ferrando for their support in recruitment and data collection.  ... 
doi:10.1016/j.cmpb.2020.105668 pmid:32755754 fatcat:vxsmmndprjcvtneerighkedeq4

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  : application to prediction of upstaged ductal carcinoma in situ using mammographic features [10575-26] 10575 0S Deep learning in breast cancer risk assessment: evaluation of fine-tuned convolutional  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Artificial Intelligence in Medical Imaging of the Breast

Yu-Meng Lei, Miao Yin, Mei-Hui Yu, Jing Yu, Shu-E Zeng, Wen-Zhi Lv, Jun Li, Hua-Rong Ye, Xin-Wu Cui, Christoph F. Dietrich
2021 Frontiers in Oncology  
density assessment; and breast cancer risk assessment.  ...  This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast  ...  ACKNOWLEDGMENTS I would like to extend my sincere gratitude to my colleagues for their help in the completion of this article and the reviewers for reviewing my article.  ... 
doi:10.3389/fonc.2021.600557 fatcat:5tphtisnhnd33c3e5oycvywnee

Deep Learning Techniques For Improving Breast Cancer Detection And Diagnosis

Amira Hassan Abed
2022 International journal of advanced networking and applications  
In this paper, we aim to introduce a survey on the applications of deep learning for breast cancer detection and diagnosis to provide an overview of the progress in this field.  ...  In the survey, we firstly provide an overview on deep learning and the popular architectures used for breast cancer detection and diagnosis.  ...  segmentation Mammographic CNN End-to-end DDSM [46] 28 Kallenberg et al. [145] segmentation & risk scoring Mammographic SSAE End-to-end FFDM [160] 29 Dhungel et al. [146] Mass segmentation Mammographic  ... 
doi:10.35444/ijana.2022.13606 fatcat:ykq5vllhhvdtjaiakqyl6dgxqy

Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
2021 Multimedia tools and applications  
Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision.  ...  Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease.  ...  CNN based CAD system AUC Ionescu et al. 2019 [59] Private data set CNN Breast density estimation and risk scoring MIAS: Mammographic Image Analysis Society dataset; DDSM: Digital Database  ... 
doi:10.1007/s11042-021-10707-4 pmid:33841033 pmcid:PMC8023554 fatcat:cm522go4nbdbnglgzpw4nu7tbi

Front Matter: Volume 9785

2016 Medical Imaging 2016: Computer-Aided Diagnosis  
Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  using a Base 36 numbering system employing both numerals and letters.  ...  nodule detection system for CT images using synthetic minority oversampling [9785-16] BREAST 9785 0I Quantification of mammographic masking risk with volumetric breast density maps: how to select  ... 
doi:10.1117/12.2240961 dblp:conf/micad/X16 fatcat:b5addnksdrgp3ixwvbjt53xeqe
« Previous Showing results 1 — 15 out of 116 results