Literature DB >> 30216858

A review of computer aided detection in mammography.

Janine Katzen1, Katerina Dodelzon2.   

Abstract

Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography ranges between 70 and 90%. Computer aided detection (CAD) is an artificial intelligence (AI) technique that utilizes pattern recognition to highlight suspicious features on imaging and marks them for the radiologist to review and interpret. It aims to decrease oversights made by interpreting radiologists. Here we review the efficacy of CAD and potential future directions.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computer aided detection; Mammography

Mesh:

Year:  2018        PMID: 30216858     DOI: 10.1016/j.clinimag.2018.08.014

Source DB:  PubMed          Journal:  Clin Imaging        ISSN: 0899-7071            Impact factor:   1.605


  15 in total

Review 1.  Deep learning in breast radiology: current progress and future directions.

Authors:  William C Ou; Dogan Polat; Basak E Dogan
Journal:  Eur Radiol       Date:  2021-01-15       Impact factor: 5.315

2.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

Review 3.  CAD and AI for breast cancer-recent development and challenges.

Authors:  Heang-Ping Chan; Ravi K Samala; Lubomir M Hadjiiski
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

4.  Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features.

Authors:  Meredith A Jones; Rowzat Faiz; Yuchen Qiu; Bin Zheng
Journal:  Phys Med Biol       Date:  2022-02-21       Impact factor: 3.609

5.  Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis.

Authors:  Victor Dahlblom; Ingvar Andersson; Kristina Lång; Anders Tingberg; Sophia Zackrisson; Magnus Dustler
Journal:  Radiol Artif Intell       Date:  2021-09-01

Review 6.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

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

7.  Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.

Authors:  Morteza Heidari; Sivaramakrishnan Lakshmivarahan; Seyedehnafiseh Mirniaharikandehei; Gopichandh Danala; Sai Kiran R Maryada; Hong Liu; Bin Zheng
Journal:  IEEE Trans Biomed Eng       Date:  2021-08-19       Impact factor: 4.756

Review 8.  A review on methods for diagnosis of breast cancer cells and tissues.

Authors:  Ziyu He; Zhu Chen; Miduo Tan; Sauli Elingarami; Yuan Liu; Taotao Li; Yan Deng; Nongyue He; Song Li; Juan Fu; Wen Li
Journal:  Cell Prolif       Date:  2020-06-12       Impact factor: 6.831

Review 9.  The Current State of Artificial Intelligence in Medical Imaging and Nuclear Medicine.

Authors:  Louise I T Lee; Senthooran Kanthasamy; Radha S Ayyalaraju; Rakesh Ganatra
Journal:  BJR Open       Date:  2019-10-16

10.  Keratoconus detection of changes using deep learning of colour-coded maps.

Authors:  Xu Chen; Jiaxin Zhao; Katja C Iselin; Davide Borroni; Davide Romano; Akilesh Gokul; Charles N J McGhee; Yitian Zhao; Mohammad-Reza Sedaghat; Hamed Momeni-Moghaddam; Mohammed Ziaei; Stephen Kaye; Vito Romano; Yalin Zheng
Journal:  BMJ Open Ophthalmol       Date:  2021-07-13
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