195 related articles for article (PubMed ID: 34847043)
1. Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists.
Amorim JP; Abreu PH; Fernandez A; Reyes M; Santos J; Abreu MH
IEEE Rev Biomed Eng; 2023; 16():192-207. PubMed ID: 34847043
[TBL] [Abstract][Full Text] [Related]
2. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization.
Papadimitroulas P; Brocki L; Christopher Chung N; Marchadour W; Vermet F; Gaubert L; Eleftheriadis V; Plachouris D; Visvikis D; Kagadis GC; Hatt M
Phys Med; 2021 Mar; 83():108-121. PubMed ID: 33765601
[TBL] [Abstract][Full Text] [Related]
3. Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.
Chereda H; Bleckmann A; Menck K; Perera-Bel J; Stegmaier P; Auer F; Kramer F; Leha A; Beißbarth T
Genome Med; 2021 Mar; 13(1):42. PubMed ID: 33706810
[TBL] [Abstract][Full Text] [Related]
4. Introduction to Machine Learning, Neural Networks, and Deep Learning.
Choi RY; Coyner AS; Kalpathy-Cramer J; Chiang MF; Campbell JP
Transl Vis Sci Technol; 2020 Feb; 9(2):14. PubMed ID: 32704420
[TBL] [Abstract][Full Text] [Related]
5. Research and Application of Ancient Chinese Pattern Restoration Based on Deep Convolutional Neural Network.
Fu X
Comput Intell Neurosci; 2021; 2021():2691346. PubMed ID: 34925485
[TBL] [Abstract][Full Text] [Related]
6. Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach.
Araújo ALD; da Silva VM; Kudo MS; de Souza ESC; Saldivia-Siracusa C; Giraldo-Roldán D; Lopes MA; Vargas PA; Khurram SA; Pearson AT; Kowalski LP; de Carvalho ACPLF; Santos-Silva AR; Moraes MC
J Oral Pathol Med; 2023 Feb; 52(2):109-118. PubMed ID: 36599081
[TBL] [Abstract][Full Text] [Related]
7. Role of Machine Learning and Artificial Intelligence in Interventional Oncology.
D'Amore B; Smolinski-Zhao S; Daye D; Uppot RN
Curr Oncol Rep; 2021 Apr; 23(6):70. PubMed ID: 33880651
[TBL] [Abstract][Full Text] [Related]
8. Medical data science in rhinology: Background and implications for clinicians.
Jun YJ; Jung J; Lee HM
Am J Otolaryngol; 2020; 41(6):102627. PubMed ID: 32682191
[TBL] [Abstract][Full Text] [Related]
9. Deep learning in cancer diagnosis, prognosis and treatment selection.
Tran KA; Kondrashova O; Bradley A; Williams ED; Pearson JV; Waddell N
Genome Med; 2021 Sep; 13(1):152. PubMed ID: 34579788
[TBL] [Abstract][Full Text] [Related]
10. Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.
Geras KJ; Mann RM; Moy L
Radiology; 2019 Nov; 293(2):246-259. PubMed ID: 31549948
[TBL] [Abstract][Full Text] [Related]
11. Current and emerging artificial intelligence applications for pediatric abdominal imaging.
Dillman JR; Somasundaram E; Brady SL; He L
Pediatr Radiol; 2022 Oct; 52(11):2139-2148. PubMed ID: 33844048
[TBL] [Abstract][Full Text] [Related]
12. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.
Buchlak QD; Esmaili N; Leveque JC; Bennett C; Farrokhi F; Piccardi M
J Clin Neurosci; 2021 Jul; 89():177-198. PubMed ID: 34119265
[TBL] [Abstract][Full Text] [Related]
13. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI.
Tjoa E; Guan C
IEEE Trans Neural Netw Learn Syst; 2021 Nov; 32(11):4793-4813. PubMed ID: 33079674
[TBL] [Abstract][Full Text] [Related]
14. Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.
Banegas-Luna AJ; Peña-García J; Iftene A; Guadagni F; Ferroni P; Scarpato N; Zanzotto FM; Bueno-Crespo A; Pérez-Sánchez H
Int J Mol Sci; 2021 Apr; 22(9):. PubMed ID: 33922356
[TBL] [Abstract][Full Text] [Related]
15. The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis.
Ranjan R; Partl R; Erhart R; Kurup N; Schnidar H
Comput Biol Med; 2021 Dec; 139():104952. PubMed ID: 34739967
[TBL] [Abstract][Full Text] [Related]
16. Artificial intelligence 101 for veterinary diagnostic imaging.
Hespel AM; Zhang Y; Basran PS
Vet Radiol Ultrasound; 2022 Dec; 63 Suppl 1():817-827. PubMed ID: 36514230
[TBL] [Abstract][Full Text] [Related]
17. Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.
Barragán-Montero A; Bibal A; Dastarac MH; Draguet C; Valdés G; Nguyen D; Willems S; Vandewinckele L; Holmström M; Löfman F; Souris K; Sterpin E; Lee JA
Phys Med Biol; 2022 May; 67(11):. PubMed ID: 35421855
[TBL] [Abstract][Full Text] [Related]
18. Understanding deep learning - challenges and prospects.
Adnan N; Umer F
J Pak Med Assoc; 2022 Feb; 72(Suppl 1)(2):S59-S63. PubMed ID: 35202373
[TBL] [Abstract][Full Text] [Related]
19. AI applications to medical images: From machine learning to deep learning.
Castiglioni I; Rundo L; Codari M; Di Leo G; Salvatore C; Interlenghi M; Gallivanone F; Cozzi A; D'Amico NC; Sardanelli F
Phys Med; 2021 Mar; 83():9-24. PubMed ID: 33662856
[TBL] [Abstract][Full Text] [Related]
20. Machine learning in medicine: what clinicians should know.
Ting Sim JZ; Fong QW; Huang W; Tan CH
Singapore Med J; 2023 Feb; 64(2):91-97. PubMed ID: 34005847
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]