Literature DB >> 32030660

Deep Learning in Medical Image Analysis.

Heang-Ping Chan1, Ravi K Samala2, Lubomir M Hadjiiski2, Chuan Zhou2.   

Abstract

Deep learning is the state-of-the-art machine learning approach. The success of deep learning in many pattern recognition applications has brought excitement and high expectations that deep learning, or artificial intelligence (AI), can bring revolutionary changes in health care. Early studies of deep learning applied to lesion detection or classification have reported superior performance compared to those by conventional techniques or even better than radiologists in some tasks. The potential of applying deep-learning-based medical image analysis to computer-aided diagnosis (CAD), thus providing decision support to clinicians and improving the accuracy and efficiency of various diagnostic and treatment processes, has spurred new research and development efforts in CAD. Despite the optimism in this new era of machine learning, the development and implementation of CAD or AI tools in clinical practice face many challenges. In this chapter, we will discuss some of these issues and efforts needed to develop robust deep-learning-based CAD tools and integrate these tools into the clinical workflow, thereby advancing towards the goal of providing reliable intelligent aids for patient care.

Entities:  

Keywords:  Artificial intelligence; Big data; Computer-aided diagnosis; Deep learning; Interpretable AI; Machine learning; Medical imaging; Quality assurance; Transfer learning; Validation

Mesh:

Year:  2020        PMID: 32030660      PMCID: PMC7442218          DOI: 10.1007/978-3-030-33128-3_1

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   2.622


  33 in total

1.  A fast learning algorithm for deep belief nets.

Authors:  Geoffrey E Hinton; Simon Osindero; Yee-Whye Teh
Journal:  Neural Comput       Date:  2006-07       Impact factor: 2.026

2.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

Review 3.  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 4.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 5.  A survey on deep learning in medical image analysis.

Authors:  Geert Litjens; Thijs Kooi; Babak Ehteshami Bejnordi; Arnaud Arindra Adiyoso Setio; Francesco Ciompi; Mohsen Ghafoorian; Jeroen A W M van der Laak; Bram van Ginneken; Clara I Sánchez
Journal:  Med Image Anal       Date:  2017-07-26       Impact factor: 8.545

6.  DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning.

Authors:  Ke Yan; Xiaosong Wang; Le Lu; Ronald M Summers
Journal:  J Med Imaging (Bellingham)       Date:  2018-07-20

7.  Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

Authors:  Ravi K Samala; Heang-Ping Chan; Lubomir M Hadjiiski; Mark A Helvie; Kenny H Cha; Caleb D Richter
Journal:  Phys Med Biol       Date:  2017-11-10       Impact factor: 3.609

8.  Impact of computer-aided detection systems on radiologist accuracy with digital mammography.

Authors:  Elodia B Cole; Zheng Zhang; Helga S Marques; R Edward Hendrick; Martin J Yaffe; Etta D Pisano
Journal:  AJR Am J Roentgenol       Date:  2014-10       Impact factor: 3.959

9.  Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.

Authors:  John R Zech; Marcus A Badgeley; Manway Liu; Anthony B Costa; Joseph J Titano; Eric Karl Oermann
Journal:  PLoS Med       Date:  2018-11-06       Impact factor: 11.069

10.  Clinically applicable deep learning for diagnosis and referral in retinal disease.

Authors:  Jeffrey De Fauw; Joseph R Ledsam; Bernardino Romera-Paredes; Stanislav Nikolov; Nenad Tomasev; Sam Blackwell; Harry Askham; Xavier Glorot; Brendan O'Donoghue; Daniel Visentin; George van den Driessche; Balaji Lakshminarayanan; Clemens Meyer; Faith Mackinder; Simon Bouton; Kareem Ayoub; Reena Chopra; Dominic King; Alan Karthikesalingam; Cían O Hughes; Rosalind Raine; Julian Hughes; Dawn A Sim; Catherine Egan; Adnan Tufail; Hugh Montgomery; Demis Hassabis; Geraint Rees; Trevor Back; Peng T Khaw; Mustafa Suleyman; Julien Cornebise; Pearse A Keane; Olaf Ronneberger
Journal:  Nat Med       Date:  2018-08-13       Impact factor: 53.440

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  47 in total

1.  Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study.

Authors:  Ying Hou; Yi-Hong Zhang; Jie Bao; Mei-Ling Bao; Guang Yang; Hai-Bin Shi; Yang Song; Yu-Dong Zhang
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-21       Impact factor: 9.236

Review 2.  The use of deep learning technology for the detection of optic neuropathy.

Authors:  Mei Li; Chao Wan
Journal:  Quant Imaging Med Surg       Date:  2022-03

3.  Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer.

Authors:  Guoping Cheng; Fuchuang Zhang; Yishi Xing; Xingyi Hu; He Zhang; Shiting Chen; Mengdao Li; Chaolong Peng; Guangtai Ding; Dadong Zhang; Peilin Chen; Qingxin Xia; Meijuan Wu
Journal:  Front Immunol       Date:  2022-07-01       Impact factor: 8.786

4.  Ultrasonic Image Feature Analysis under Deep Learning Algorithm to Evaluate the Efficacy of Drug-Coated Balloon for Treatment of Arteriosclerotic Occlusion.

Authors:  Yuchao Zhang; Gang Xu; Maozhen Chen; Ziliang Chen; Mingyang Shen; Ping Wang
Journal:  Comput Math Methods Med       Date:  2022-05-31       Impact factor: 2.809

5.  Deep Learning for Approaching Hepatocellular Carcinoma Ultrasound Screening Dilemma: Identification of α-Fetoprotein-Negative Hepatocellular Carcinoma From Focal Liver Lesion Found in High-Risk Patients.

Authors:  Wei-Bin Zhang; Si-Ze Hou; Yan-Ling Chen; Feng Mao; Yi Dong; Jian-Gang Chen; Wen-Ping Wang
Journal:  Front Oncol       Date:  2022-05-26       Impact factor: 5.738

6.  Prediction of the composition of urinary stones using deep learning.

Authors:  Ui Seok Kim; Hyo Sang Kwon; Wonjong Yang; Wonchul Lee; Changil Choi; Jong Keun Kim; Seong Ho Lee; Dohyoung Rim; Jun Hyun Han
Journal:  Investig Clin Urol       Date:  2022-05-25

7.  Survival stratification for colorectal cancer via multi-omics integration using an autoencoder-based model.

Authors:  Hu Song; Chengwei Ruan; Yixin Xu; Teng Xu; Ruizhi Fan; Tao Jiang; Meng Cao; Jun Song
Journal:  Exp Biol Med (Maywood)       Date:  2021-12-14

8.  Automated detection of brain metastases on non-enhanced CT using single-shot detectors.

Authors:  Shimpei Kato; Shiori Amemiya; Hidemasa Takao; Hiroshi Yamashita; Naoya Sakamoto; Osamu Abe
Journal:  Neuroradiology       Date:  2021-06-10       Impact factor: 2.804

9.  A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.

Authors:  Unais Sait; Gokul Lal K V; Sanjana Shivakumar; Tarun Kumar; Rahul Bhaumik; Sunny Prajapati; Kriti Bhalla; Anaghaa Chakrapani
Journal:  Appl Soft Comput       Date:  2021-05-26       Impact factor: 6.725

Review 10.  Tumor-Associated Tertiary Lymphoid Structures: From Basic and Clinical Knowledge to Therapeutic Manipulation.

Authors:  Charlotte Domblides; Juliette Rochefort; Clémence Riffard; Marylou Panouillot; Géraldine Lescaille; Jean-Luc Teillaud; Véronique Mateo; Marie-Caroline Dieu-Nosjean
Journal:  Front Immunol       Date:  2021-06-30       Impact factor: 7.561

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