Literature DB >> 31846401

Harnessing the Power of Deep Learning to Assess Breast Cancer Risk.

Manisha Bahl1.   

Abstract

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Year:  2019        PMID： 31846401      PMCID： PMC6996607          DOI： 10.1148/radiol.2019192471

Source DB:  PubMed          Journal:  Radiology        ISSN： 0033-8419            Impact factor:   11.105


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  10 in total

Review 1.  Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

Authors:  Maciej A Mazurowski; Mateusz Buda; Ashirbani Saha; Mustafa R Bashir
Journal:  J Magn Reson Imaging       Date:  2018-12-21       Impact factor: 4.813

Review 2.  Implementing Machine Learning in Radiology Practice and Research.

Authors:  Marc Kohli; Luciano M Prevedello; Ross W Filice; J Raymond Geis
Journal:  AJR Am J Roentgenol       Date:  2017-01-26       Impact factor: 3.959

3.  Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction.

Authors:  Karin Dembrower; Yue Liu; Hossein Azizpour; Martin Eklund; Kevin Smith; Peter Lindholm; Fredrik Strand
Journal:  Radiology       Date:  2019-12-17       Impact factor: 11.105

Review 4.  Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

Authors:  Jeremy R Burt; Neslisah Torosdagli; Naji Khosravan; Harish RaviPrakash; Aliasghar Mortazi; Fiona Tissavirasingham; Sarfaraz Hussein; Ulas Bagci
Journal:  Br J Radiol       Date:  2018-04-10       Impact factor: 3.039

Review 5.  Assessing women at high risk of breast cancer: a review of risk assessment models.

Authors:  Eitan Amir; Orit C Freedman; Bostjan Seruga; D Gareth Evans
Journal:  J Natl Cancer Inst       Date:  2010-04-28       Impact factor: 13.506

6.  A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction.

Authors:  Adam Yala; Constance Lehman; Tal Schuster; Tally Portnoi; Regina Barzilay
Journal:  Radiology       Date:  2019-05-07       Impact factor: 11.105

7.  Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.

Authors:  Richard Ha; Peter Chang; Jenika Karcich; Simukayi Mutasa; Eduardo Pascual Van Sant; Michael Z Liu; Sachin Jambawalikar
Journal:  Acad Radiol       Date:  2018-07-31       Impact factor: 3.173

8.  Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study.

Authors:  Brian L Sprague; Emily F Conant; Tracy Onega; Michael P Garcia; Elisabeth F Beaber; Sally D Herschorn; Constance D Lehman; Anna N A Tosteson; Ronilda Lacson; Mitchell D Schnall; Despina Kontos; Jennifer S Haas; Donald L Weaver; William E Barlow
Journal:  Ann Intern Med       Date:  2016-07-19       Impact factor: 25.391

9.  Long-term Accuracy of Breast Cancer Risk Assessment Combining Classic Risk Factors and Breast Density.

Authors:  Adam R Brentnall; Jack Cuzick; Diana S M Buist; Erin J Aiello Bowles
Journal:  JAMA Oncol       Date:  2018-09-13       Impact factor: 31.777

10.  Deep convolutional neural networks for mammography: advances, challenges and applications.

Authors:  Dina Abdelhafiz; Clifford Yang; Reda Ammar; Sheida Nabavi
Journal:  BMC Bioinformatics       Date:  2019-06-06       Impact factor: 3.169
  10 in total
  3 in total

Review 1.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

Authors:  Manisha Bahl
Journal:  J Breast Imaging       Date:  2020-06-19

Review 2.  Assessing Risk of Breast Cancer: A Review of Risk Prediction Models.

Authors:  Geunwon Kim; Manisha Bahl
Journal:  J Breast Imaging       Date:  2021-02-19

3.  Dynamic Changes of Convolutional Neural Network-based Mammographic Breast Cancer Risk Score Among Women Undergoing Chemoprevention Treatment.

Authors:  Haley Manley; Simukayi Mutasa; Peter Chang; Elise Desperito; Katherine Crew; Richard Ha
Journal:  Clin Breast Cancer       Date:  2020-11-17       Impact factor: 3.225
  3 in total

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