Literature DB >> 27481324

Machine learning approaches in medical image analysis: From detection to diagnosis.

Marleen de Bruijne1.   

Abstract

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.
Copyright © 2016 Elsevier B.V. All rights reserved.

Keywords:  Classification; Computer aided diagnosis; Machine learning; Transfer learning

Mesh:

Year:  2016        PMID: 27481324     DOI: 10.1016/j.media.2016.06.032

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  43 in total

1.  Open access image repositories: high-quality data to enable machine learning research.

Authors:  F Prior; J Almeida; P Kathiravelu; T Kurc; K Smith; T J Fitzgerald; J Saltz
Journal:  Clin Radiol       Date:  2019-04-28       Impact factor: 2.350

2.  Medical students' attitude towards artificial intelligence: a multicentre survey.

Authors:  D Pinto Dos Santos; D Giese; S Brodehl; S H Chon; W Staab; R Kleinert; D Maintz; B Baeßler
Journal:  Eur Radiol       Date:  2018-07-06       Impact factor: 5.315

Review 3.  Machine Learning Approaches in Cardiovascular Imaging.

Authors:  Mir Henglin; Gillian Stein; Pavel V Hushcha; Jasper Snoek; Alexander B Wiltschko; Susan Cheng
Journal:  Circ Cardiovasc Imaging       Date:  2017-10       Impact factor: 7.792

4.  A proof of concept for epidemiological research using structured reporting with pulmonary embolism as a use case.

Authors:  Daniel Pinto Dos Santos; Sonja Scheibl; Gordon Arnhold; Aline Maehringer-Kunz; Christoph Düber; Peter Mildenberger; Roman Kloeckner
Journal:  Br J Radiol       Date:  2018-06-05       Impact factor: 3.039

Review 5.  Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.

Authors:  Amitava Halder; Debangshu Dey; Anup K Sadhu
Journal:  J Digit Imaging       Date:  2020-06       Impact factor: 4.056

Review 6.  Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?

Authors:  Ryan W Stidham; Kento Takenaka
Journal:  Gastroenterology       Date:  2022-01-04       Impact factor: 22.682

7.  Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection.

Authors:  Samuel W Remedios; Zihao Wu; Camilo Bermudez; Cailey I Kerley; Snehashis Roy; Mayur B Patel; John A Butman; Bennett A Landman; Dzung L Pham
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-03-10

8.  Feasibility of fully automated classification of whole slide images based on deep learning.

Authors:  Kyung-Ok Cho; Sung Hak Lee; Hyun-Jong Jang
Journal:  Korean J Physiol Pharmacol       Date:  2020-12-20       Impact factor: 2.016

Review 9.  An introduction to machine learning and analysis of its use in rheumatic diseases.

Authors:  Kathryn M Kingsmore; Christopher E Puglisi; Amrie C Grammer; Peter E Lipsky
Journal:  Nat Rev Rheumatol       Date:  2021-11-02       Impact factor: 20.543

10.  Deep learning prediction of mild cognitive impairment conversion to Alzheimer's disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI.

Authors:  Ethan Ocasio; Tim Q Duong
Journal:  PeerJ Comput Sci       Date:  2021-05-25
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