Literature DB >> 35402947

Lung Nodule Malignancy Prediction From Longitudinal CT Scans With Siamese Convolutional Attention Networks.

Benjamin P Veasey1, Justin Broadhead1, Michael Dahle1, Albert Seow1, Amir A Amini1.   

Abstract

Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure.
Methods: Our proposed framework is evaluated for single- and multi-time-point classification to explore the value that temporal information, such as nodule growth, adds to malignancy prediction.
Results: Our results show that the proposed method outperforms a comparable 3-D network with less than half the parameters on single-time-point classification and further achieves performance gains on multi-time-point classification. Conclusions: Attention-based, Siamese 2-D pre-trained CNNs lead to fast training times and are effective for malignancy prediction from single-time-point or multiple-time-point imaging data.

Entities:  

Keywords:  Lung cancer diagnosis; X-ray CT; deep learning; longitudinal studies; siamese networks

Year:  2020        PMID: 35402947      PMCID: PMC8975149          DOI: 10.1109/OJEMB.2020.3023614

Source DB:  PubMed          Journal:  IEEE Open J Eng Med Biol        ISSN: 2644-1276


  13 in total

1.  Multi-scale Convolutional Neural Networks for Lung Nodule Classification.

Authors:  Wei Shen; Mu Zhou; Feng Yang; Caiyun Yang; Jie Tian
Journal:  Inf Process Med Imaging       Date:  2015

2.  Lung nodule classification using deep Local-Global networks.

Authors:  Mundher Al-Shabi; Boon Leong Lan; Wai Yee Chan; Kwan-Hoong Ng; Maxine Tan
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-04-24       Impact factor: 2.924

3.  Pulmonary nodule classification with deep residual networks.

Authors:  Aiden Nibali; Zhen He; Dennis Wollersheim
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-05-13       Impact factor: 2.924

4.  Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans.

Authors:  M Mehdi Farhangi; Nicholas Petrick; Berkman Sahiner; Hichem Frigui; Amir A Amini; Aria Pezeshk
Journal:  Med Phys       Date:  2020-03-18       Impact factor: 4.071

5.  Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping.

Authors:  Yiming Lei; Yukun Tian; Hongming Shan; Junping Zhang; Ge Wang; Mannudeep K Kalra
Journal:  Med Image Anal       Date:  2019-12-12       Impact factor: 8.545

6.  Reduced lung-cancer mortality with low-dose computed tomographic screening.

Authors:  Denise R Aberle; Amanda M Adams; Christine D Berg; William C Black; Jonathan D Clapp; Richard M Fagerstrom; Ilana F Gareen; Constantine Gatsonis; Pamela M Marcus; JoRean D Sicks
Journal:  N Engl J Med       Date:  2011-06-29       Impact factor: 91.245

7.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

Authors:  Samuel G Armato; Geoffrey McLennan; Luc Bidaut; Michael F McNitt-Gray; Charles R Meyer; Anthony P Reeves; Binsheng Zhao; Denise R Aberle; Claudia I Henschke; Eric A Hoffman; Ella A Kazerooni; Heber MacMahon; Edwin J R Van Beeke; David Yankelevitz; Alberto M Biancardi; Peyton H Bland; Matthew S Brown; Roger M Engelmann; Gary E Laderach; Daniel Max; Richard C Pais; David P Y Qing; Rachael Y Roberts; Amanda R Smith; Adam Starkey; Poonam Batrah; Philip Caligiuri; Ali Farooqi; Gregory W Gladish; C Matilda Jude; Reginald F Munden; Iva Petkovska; Leslie E Quint; Lawrence H Schwartz; Baskaran Sundaram; Lori E Dodd; Charles Fenimore; David Gur; Nicholas Petrick; John Freymann; Justin Kirby; Brian Hughes; Alessi Vande Casteele; Sangeeta Gupte; Maha Sallamm; Michael D Heath; Michael H Kuhn; Ekta Dharaiya; Richard Burns; David S Fryd; Marcos Salganicoff; Vikram Anand; Uri Shreter; Stephen Vastagh; Barbara Y Croft
Journal:  Med Phys       Date:  2011-02       Impact factor: 4.071

8.  Predicting Malignant Nodules from Screening CT Scans.

Authors:  Samuel Hawkins; Hua Wang; Ying Liu; Alberto Garcia; Olya Stringfield; Henry Krewer; Qian Li; Dmitry Cherezov; Robert A Gatenby; Yoganand Balagurunathan; Dmitry Goldgof; Matthew B Schabath; Lawrence Hall; Robert J Gillies
Journal:  J Thorac Oncol       Date:  2016-07-13       Impact factor: 15.609

9.  End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.

Authors:  Diego Ardila; Atilla P Kiraly; Sujeeth Bharadwaj; Bokyung Choi; Joshua J Reicher; Lily Peng; Daniel Tse; Mozziyar Etemadi; Wenxing Ye; Greg Corrado; David P Naidich; Shravya Shetty
Journal:  Nat Med       Date:  2019-05-20       Impact factor: 53.440

10.  Highly accurate model for prediction of lung nodule malignancy with CT scans.

Authors:  Jason L Causey; Junyu Zhang; Shiqian Ma; Bo Jiang; Jake A Qualls; David G Politte; Fred Prior; Shuzhong Zhang; Xiuzhen Huang
Journal:  Sci Rep       Date:  2018-06-18       Impact factor: 4.379

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  2 in total

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2.  Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.

Authors:  Yoganand Balagurunathan; Andrew Beers; Michael Mcnitt-Gray; Lubomir Hadjiiski; Sandy Napel; Dmitry Goldgof; Gustavo Perez; Pablo Arbelaez; Alireza Mehrtash; Tina Kapur; Ehwa Yang; Jung Won Moon; Gabriel Bernardino Perez; Ricard Delgado-Gonzalo; M Mehdi Farhangi; Amir A Amini; Renkun Ni; Xue Feng; Aditya Bagari; Kiran Vaidhya; Benjamin Veasey; Wiem Safta; Hichem Frigui; Joseph Enguehard; Ali Gholipour; Laura Silvana Castillo; Laura Alexandra Daza; Paul Pinsky; Jayashree Kalpathy-Cramer; Keyvan Farahani
Journal:  IEEE Trans Med Imaging       Date:  2021-11-30       Impact factor: 11.037

  2 in total

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