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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1993 1
1995 4
1996 1
1997 2
1998 3
1999 1
2000 1
2001 4
2004 4
2005 2
2006 4
2007 4
2008 5
2009 6
2010 2
2011 9
2012 8
2013 6
2014 9
2015 15
2016 11
2017 15
2018 18
2019 19
2020 21
2021 20
2022 21
2023 19
2024 5

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211 results

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Page 1
Multiparametric MRI and Radiomics for the Prediction of HER2-Zero, -Low, and -Positive Breast Cancers.
Ramtohul T, Djerroudi L, Lissavalid E, Nhy C, Redon L, Ikni L, Djelouah M, Journo G, Menet E, Cabel L, Malhaire C, Tardivon A. Ramtohul T, et al. Radiology. 2023 Aug;308(2):e222646. doi: 10.1148/radiol.222646. Radiology. 2023. PMID: 37526540
Tumor segmentation and radiomic feature extraction were performed on T2-weighted and dynamic contrast-enhanced T1-weighted images. Unsupervised correlation analysis of reproducible features and least absolute shrinkage and selector operation were used …
Tumor segmentation and radiomic feature extraction were performed on T2-weighted and dynamic contrast-enhanced T1-weighted …
Image segmentation feature selection and pattern classification for mammographic microcalcifications.
Fu JC, Lee SK, Wong ST, Yeh JY, Wang AH, Wu HK. Fu JC, et al. Comput Med Imaging Graph. 2005 Sep;29(6):419-29. doi: 10.1016/j.compmedimag.2005.03.002. Comput Med Imaging Graph. 2005. PMID: 16002263
From these features, a sequential forward search (SFS) algorithm selects the classification input vector, which consists of features sensitive only to microcalcifications. ...The SVM outperformed the GRNN, whether or not the input vectors first underwent SFS …
From these features, a sequential forward search (SFS) algorithm selects the classification input vector, which consists of …
Breast Cancer Cryoablation Fundamentals Past and Present: Technique Optimization and Imaging Pearls.
Huang ML, Tomkovich K, Lane DL, Katta R, Candelaria RP, Santiago L. Huang ML, et al. Acad Radiol. 2023 Oct;30(10):2383-2395. doi: 10.1016/j.acra.2023.05.019. Epub 2023 Jul 15. Acad Radiol. 2023. PMID: 37455177 Review.
As the de-escalation of surgical treatment for breast cancer continues, nonsurgical treatment for early-stage breast cancer with favorable ancillary features (low grade, positivity for hormone receptors) is being explored. ...Understanding the indications and …
As the de-escalation of surgical treatment for breast cancer continues, nonsurgical treatment for early-stage breast cancer wi …
Levels Propagation Approach to Image Segmentation: Application to Breast MR Images.
Bouchebbah F, Slimani H. Bouchebbah F, et al. J Digit Imaging. 2019 Jun;32(3):433-449. doi: 10.1007/s10278-018-00171-2. J Digit Imaging. 2019. PMID: 30706211 Free PMC article.
Accurate segmentation of a breast tumor region is fundamental for treatment. Magnetic resonance imaging (MRI) is a widely used diagnostic tool. In this paper, a new semi-automatic segmentation approach for MRI breast tumor segmentation …
Accurate segmentation of a breast tumor region is fundamental for treatment. Magnetic resonance imaging (MRI) is a wide …
Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.
Ma M, Gan L, Jiang Y, Qin N, Li C, Zhang Y, Wang X. Ma M, et al. Comput Math Methods Med. 2021 Aug 10;2021:2140465. doi: 10.1155/2021/2140465. eCollection 2021. Comput Math Methods Med. 2021. PMID: 34422088 Free PMC article.
PURPOSE: To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non- …
PURPOSE: To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imagin …
Breast Masses Detection and Segmentation in Full-Field Digital Mammograms using Unified Convolution Neural Network.
Rajasree PM, Jatti A, Santosh D, Desai U, Krishnappa VD. Rajasree PM, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1002-1007. doi: 10.1109/EMBC48229.2022.9871866. Annu Int Conf IEEE Eng Med Biol Soc. 2022. PMID: 36085669
Further RRS aka Random Region Selection mechanism is applied for data augmentation approach and high-level feature map is implied to achieve the high prediction. ...It introduces a novel module at the convolution layer to aim for a high-level feature map in o …
Further RRS aka Random Region Selection mechanism is applied for data augmentation approach and high-level feature map is impl …
A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.
Lu W, Li Z, Chu J. Lu W, et al. Comput Biol Med. 2017 Apr 1;83:157-165. doi: 10.1016/j.compbiomed.2017.03.002. Epub 2017 Mar 6. Comput Biol Med. 2017. PMID: 28282591
We extracted morphological, various texture, and Gabor features. To clarify the feature subsets' physical meaning, subspaces are built by combining morphological features with each kind of texture or Gabor feature. We tested our proposal using a manual …
We extracted morphological, various texture, and Gabor features. To clarify the feature subsets' physical meaning, subspaces a …
A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.
Tan M, Pu J, Zheng B. Tan M, et al. Med Phys. 2014 Aug;41(8):081906. doi: 10.1118/1.4890080. Med Phys. 2014. PMID: 25086537 Free PMC article.
PURPOSE: Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. ...Besides computing features from the original images, the autho …
PURPOSE: Selecting optimal features from a large image feature pool remains a major challenge in developing comp …
Segmentation of breast ultrasound image with semantic classification of superpixels.
Huang Q, Huang Y, Luo Y, Yuan F, Li X. Huang Q, et al. Med Image Anal. 2020 Apr;61:101657. doi: 10.1016/j.media.2020.101657. Epub 2020 Jan 25. Med Image Anal. 2020. PMID: 32032899
Due to the poor image quality, segmentation of breast ultrasound (BUS) image remains a very challenging task. Besides, BUS image segmentation is a crucial step for further analysis. In this paper, we proposed a novel method to segment …
Due to the poor image quality, segmentation of breast ultrasound (BUS) image remains a very challenging task. Be …
Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging.
Zerouaoui H, Idri A. Zerouaoui H, et al. J Med Syst. 2021 Jan 4;45(1):8. doi: 10.1007/s10916-020-01689-1. J Med Syst. 2021. PMID: 33404910
This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten c …
This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to …
211 results