Literature DB >> 31066627

Assessing Cancer Risk from Mammograms: Deep Learning Is Superior to Conventional Risk Models.

Arkadiusz Sitek1, Jeremy M Wolfe1.   

Abstract

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Year:  2019        PMID： 31066627      PMCID： PMC6604791          DOI： 10.1148/radiol.2019190791

Source DB:  PubMed          Journal:  Radiology        ISSN： 0033-8419            Impact factor:   29.146


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  8 in total

1.  Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning.

Authors:  Jarrel C Y Seah; Jennifer S N Tang; Andy Kitchen; Frank Gaillard; Andrew F Dixon
Journal:  Radiology       Date:  2018-11-06       Impact factor: 11.105

2.  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

3.  A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast.

Authors:  Karla K Evans; Tamara Miner Haygood; Julie Cooper; Anne-Marie Culpan; Jeremy M Wolfe
Journal:  Proc Natl Acad Sci U S A       Date:  2016-08-29       Impact factor: 11.205

4.  The briefest of glances: the time course of natural scene understanding.

Authors:  Michelle R Greene; Aude Oliva
Journal:  Psychol Sci       Date:  2009-04

5.  Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.

Authors:  Ju Gang Nam; Sunggyun Park; Eui Jin Hwang; Jong Hyuk Lee; Kwang-Nam Jin; Kun Young Lim; Thienkai Huy Vu; Jae Ho Sohn; Sangheum Hwang; Jin Mo Goo; Chang Min Park
Journal:  Radiology       Date:  2018-09-25       Impact factor: 11.105

6.  Radiologists can detect the 'gist' of breast cancer before any overt signs of cancer appear.

Authors:  Patrick C Brennan; Ziba Gandomkar; Ernest U Ekpo; Kriscia Tapia; Phuong D Trieu; Sarah J Lewis; Jeremy M Wolfe; Karla K Evans
Journal:  Sci Rep       Date:  2018-06-07       Impact factor: 4.379

7.  If you don't find it often, you often don't find it: why some cancers are missed in breast cancer screening.

Authors:  Karla K Evans; Robyn L Birdwell; Jeremy M Wolfe
Journal:  PLoS One       Date:  2013-05-30       Impact factor: 3.240

8.  Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort.

Authors:  Adam R Brentnall; Elaine F Harkness; Susan M Astley; Louise S Donnelly; Paula Stavrinos; Sarah Sampson; Lynne Fox; Jamie C Sergeant; Michelle N Harvie; Mary Wilson; Ursula Beetles; Soujanya Gadde; Yit Lim; Anil Jain; Sara Bundred; Nicola Barr; Valerie Reece; Anthony Howell; Jack Cuzick; D Gareth R Evans
Journal:  Breast Cancer Res       Date:  2015-12-01       Impact factor: 6.466
  8 in total
  2 in total

1.  Melanoma in the Blink of an Eye: Pathologists' Rapid Detection, Classification, and Localization of Skin Abnormalities.

Authors:  Tad T Brunyé; Trafton Drew; Manob Jyoti Saikia; Kathleen F Kerr; Megan M Eguchi; Annie C Lee; Caitlin May; David E Elder; Joann G Elmore
Journal:  Vis cogn       Date:  2021-06-16

Review 2.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

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

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