Literature DB >> 30898263

Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.

Adrian B Levine1, Colin Schlosser2, Jasleen Grewal2, Robin Coope2, Steve J M Jones2, Stephen Yip3.   

Abstract

Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most extensively radiology and pathology, and in some cases have attained performance comparable to human experts. Furthermore, it is possible that deep learning could be used to extract data from medical images that would not be apparent by human analysis and could be used to inform on molecular status, prognosis, or treatment sensitivity. In this review, we outline the current developments and state-of-the-art in applying deep learning for cancer diagnosis, and discuss the challenges in adapting the technology for widespread clinical deployment.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  artificial intelligence; deep learning; digital pathology; machine learning; radiomics; whole slide imaging

Mesh:

Year:  2019        PMID: 30898263     DOI: 10.1016/j.trecan.2019.02.002

Source DB:  PubMed          Journal:  Trends Cancer        ISSN: 2405-8025


  30 in total

Review 1.  Current applications and future directions of deep learning in musculoskeletal radiology.

Authors:  Pauley Chea; Jacob C Mandell
Journal:  Skeletal Radiol       Date:  2019-08-04       Impact factor: 2.199

2.  Artificial intelligence in clinical research of cancers.

Authors:  Dan Shao; Yinfei Dai; Nianfeng Li; Xuqing Cao; Wei Zhao; Li Cheng; Zhuqing Rong; Lan Huang; Yan Wang; Jing Zhao
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 11.622

Review 3.  Machine learning in neuro-oncology: toward novel development fields.

Authors:  Vincenzo Di Nunno; Mario Fordellone; Giuseppe Minniti; Sofia Asioli; Alfredo Conti; Diego Mazzatenta; Damiano Balestrini; Paolo Chiodini; Raffaele Agati; Caterina Tonon; Alicia Tosoni; Lidia Gatto; Stefania Bartolini; Raffaele Lodi; Enrico Franceschi
Journal:  J Neurooncol       Date:  2022-06-28       Impact factor: 4.506

4.  Multi-needle Localization with Attention U-Net in US-guided HDR Prostate Brachytherapy.

Authors:  Yupei Zhang; Yang Lei; Richard L J Qiu; Tonghe Wang; Hesheng Wang; Ashesh B Jani; Walter J Curran; Pretesh Patel; Tian Liu; Xiaofeng Yang
Journal:  Med Phys       Date:  2020-04-03       Impact factor: 4.071

5.  A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks.

Authors:  Lior Zimmerman; Ori Zelichov; Arie Aizenmann; Zohar Barbash; Michael Vidne; Gabi Tarcic
Journal:  Sci Rep       Date:  2020-03-06       Impact factor: 4.379

6.  Introduction to machine and deep learning for medical physicists.

Authors:  Sunan Cui; Huan-Hsin Tseng; Julia Pakela; Randall K Ten Haken; Issam El Naqa
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

7.  How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction.

Authors:  Marylène Lejeune; Benoît Plancoulaine; Nicolas Elie; Ramon Bosch; Laia Fontoura; Izar de Villasante; Anna Korzyńska; Andrea Gras Navarro; Esther Sauras Colón; Carlos López
Journal:  Histochem Cell Biol       Date:  2021-08-12       Impact factor: 4.304

8.  Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases.

Authors:  Kyoung Min Kim; Tae-Young Heo; Aesul Kim; Joohee Kim; Kyu Jin Han; Jaesuk Yun; Jung Kee Min
Journal:  J Pers Med       Date:  2021-04-21

Review 9.  Requirements and reliability of AI in the medical context.

Authors:  Yoganand Balagurunathan; Ross Mitchell; Issam El Naqa
Journal:  Phys Med       Date:  2021-03-13       Impact factor: 2.685

10.  Prognostic image-based quantification of CD8CD103 T cell subsets in high-grade serous ovarian cancer patients.

Authors:  S T Paijens; A Vledder; D Loiero; E W Duiker; J Bart; A M Hendriks; M Jalving; H H Workel; H Hollema; N Werner; A Plat; G B A Wisman; R Yigit; H Arts; A J Kruse; N M de Lange; V H Koelzer; M de Bruyn; H W Nijman
Journal:  Oncoimmunology       Date:  2021-06-06       Impact factor: 8.110

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