Artificial Intelligence in Oncology: A Topical Collection in 2022-Reference-Cited by-同舟云学术

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Artificial Intelligence in Oncology: A Topical Collection in 2022

Published:2023-02-07 Issue:4 Volume:15 Page:1065
ISSN:2072-6694
Container-title:Cancers
language:en
Short-container-title:Cancers

Author:

Stadlbauer Andreas¹²^ORCID,Meyer-Bäse Anke³

Affiliation:

1. Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, 3100 St. Pölten, Austria

2. Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, 91054 Erlangen, Germany

3. Department of Scientific Computing, Florida State University, 400 Dirac Science Library, Tallahassee, FL 32306-4120, USA

Abstract

Artificial intelligence (AI) is considered one of the core technologies of the Fourth Industrial Revolution that is currently taking place [...]

Publisher

MDPI AG

Subject

Cancer Research,Oncology

Link

https://www.mdpi.com/2072-6694/15/4/1065/pdf

Reference22 articles.

1. Wu, Y., Cheng, M., Huang, S., Pei, Z., Zuo, Y., Liu, J., Yang, K., Zhu, Q., Zhang, J., and Hong, H. (2022). Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications. Cancers, 14.

2. Wong, A.N.N., He, Z., Leung, K.L., To, C.C.K., Wong, C.Y., Wong, S.C.C., Yoo, J.S., Chan, C.K.R., Chan, A.Z., and Lacambra, M.D. (2022). Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers. Cancers, 14.

3. Benning, L., Peintner, A., and Peintner, L. (2022). Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer. Cancers, 14.

4. Estevez, M., Benedum, C.M., Jiang, C., Cohen, A.B., Phadke, S., Sarkar, S., and Bozkurt, S. (2022). Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A Research-Centric Evaluation Framework. Cancers, 14.

5. Saida, T., Mori, K., Hoshiai, S., Sakai, M., Urushibara, A., Ishiguro, T., Minami, M., Satoh, T., and Nakajima, T. (2022). Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments. Cancers, 14.

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