Warning: Undefined array key "mm" in /www/wwwroot/www.ai-bt.com/si.php on line 10 Deprecated: trim(): Passing null to parameter #1 ($string) of type string is deprecated in /www/wwwroot/www.ai-bt.com/si.php on line 10 Understanding artificial intelligence based radiology studies: What is overfitting?

Literature DB >> 32387803

Understanding artificial intelligence based radiology studies: What is overfitting?

Simukayi Mutasa¹, Shawn Sun², Richard Ha³.

Abstract

Artificial intelligence (AI) is a broad umbrella term used to encompass a wide variety of subfields dedicated to creating algorithms to perform tasks that mimic human intelligence. As AI development grows closer to clinical integration, radiologists will need to become familiar with the principles of artificial intelligence to properly evaluate and use this powerful tool. This series aims to explain certain basic concepts of artificial intelligence, and their applications in medical imaging starting with a concept of overfitting.

Entities: Chemical Disease Gene Species

Keywords: Artificial intelligence; Machine learning; Overfitting

Mesh：

Year: 2020 PMID： 32387803 PMCID： PMC8150901 DOI： 10.1016/j.clinimag.2020.04.025

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

26 in total

1. Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients.

Authors: Julia Willamena Patriarche; Bradley James Erickson
Journal: J Digit Imaging Date: 2007-09 Impact factor: 4.056

2. Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists.

Authors: Saurabh Jha; Eric J Topol
Journal: JAMA Date: 2016-12-13 Impact factor: 56.272

3. Large scale deep learning for computer aided detection of mammographic lesions.

Authors: Thijs Kooi; Geert Litjens; Bram van Ginneken; Albert Gubern-Mérida; Clara I Sánchez; Ritse Mann; Ard den Heeten; Nico Karssemeijer
Journal: Med Image Anal Date: 2016-08-02 Impact factor: 8.545

4. 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

5. 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

6. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning.

Authors: Mizuho Nishio; Osamu Sugiyama; Masahiro Yakami; Syoko Ueno; Takeshi Kubo; Tomohiro Kuroda; Kaori Togashi
Journal: PLoS One Date: 2018-07-27 Impact factor: 3.240

7. 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

8. Deep learning and artificial intelligence in radiology: Current applications and future directions.

Authors: Koichiro Yasaka; Osamu Abe
Journal: PLoS Med Date: 2018-11-30 Impact factor: 11.069

9. Transfer Learning Assisted Classification and Detection of Alzheimer's Disease Stages Using 3D MRI Scans.

Authors: Muazzam Maqsood; Faria Nazir; Umair Khan; Farhan Aadil; Habibullah Jamal; Irfan Mehmood; Oh-Young Song
Journal: Sensors (Basel) Date: 2019-06-11 Impact factor: 3.576

10. Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

Authors: Jie-Zhi Cheng; Dong Ni; Yi-Hong Chou; Jing Qin; Chui-Mei Tiu; Yeun-Chung Chang; Chiun-Sheng Huang; Dinggang Shen; Chung-Ming Chen
Journal: Sci Rep Date: 2016-04-15 Impact factor: 4.379

18 in total

Review 1. Accuracy of artificial intelligence for tooth extraction decision-making in orthodontics: a systematic review and meta-analysis.

Authors: Karine Evangelista; Brunno Santos de Freitas Silva; Fernanda Paula Yamamoto-Silva; José Valladares-Neto; Maria Alves Garcia Silva; Lucia Helena Soares Cevidanes; Graziela de Luca Canto; Carla Massignan
Journal: Clin Oral Investig Date: 2022-10-21 Impact factor: 3.606

2. Classification of breast cancer histology images using MSMV-PFENet.

Authors: Linxian Liu; Wenxiang Feng; Cheng Chen; Manhua Liu; Yuan Qu; Jiamiao Yang
Journal: Sci Rep Date: 2022-10-19 Impact factor: 4.996

3. The application of artificial intelligence to support biliary atresia screening by ultrasound images: A study based on deep learning models.

Authors: Fang-Rong Hsu; Sheng-Tong Dai; Chia-Man Chou; Sheng-Yang Huang
Journal: PLoS One Date: 2022-10-19 Impact factor: 3.752

4. An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging.

Authors: Nicolò Cardobi; Alessandro Dal Palù; Federica Pedrini; Alessandro Beleù; Riccardo Nocini; Riccardo De Robertis; Andrea Ruzzenente; Roberto Salvia; Stefania Montemezzi; Mirko D'Onofrio
Journal: Cancers (Basel) Date: 2021-04-30 Impact factor: 6.639

Review 10. Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence.

Authors: Seong Ho Park; Jaesoon Choi; Jeong Sik Byeon
Journal: Korean J Radiol Date: 2021-03 Impact factor: 3.500