Crowdsourced Emphysema Assessment

Ørting, Silas Nyboe; Cheplygina, Veronika; Petersen, Jens; Thomsen, Laura H.; Wille, Mathilde Marie Winkler; Bruijne, Marleen de

doi:10.1007/978-3-319-67534-3_14

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…One approach for dealing with 3D images is to select 2D parts of the original 3D images. For example, Ørting et al (2017) andO'Neil et al (2017) select axial slices. Cheplygina et al (2016) shows patches of 2D projections in various directions in the image.…”

Section: Task Papersmentioning

confidence: 99%

A Survey of Crowdsourcing in Medical Image Analysis

Ørting

Doyle²,

Hilten

et al. 2020

HCj

Self Cite

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Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

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Section: Task Papersmentioning

confidence: 99%

A Survey of Crowdsourcing in Medical Image Analysis

Ørting

Doyle²,

Hilten

et al. 2020

HCj

Self Cite

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“…There is no prior work on collecting labels for related tasks and similarities, except in my recent pilot studies [17,18]. Approach.…”

Section: F I G U R E 2 Airway In a Chest Ct Patchmentioning

confidence: 99%

“…⊳ Localizing patches with emphysema, in chest CT images [43,44]. This is a task of medium difficulty, where I expect best results from MTL because the underlying disease affects the appearance of airways, or SBL based on recent pilot results [18].…”

Section: F I G U R E 2 Airway In a Chest Ct Patchmentioning

confidence: 99%

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CrowdDetective: Wisdom of the Crowds for Detecting Abnormalities in Medical Scans

Cheplygina

2020

JOTE

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Machine learning (ML) has great potential for early diagnosis of disease from medical scans, and at times, has even been shown to outperform experts. However, ML algorithms need large amounts of annotated data-scans with outlined abnormalitiesfor good performance. The time-consuming annotation process limits the progress of ML in this field. To address the annotation problem, multiple instance learning (MIL) algorithms were proposed, which learn from scans that have been diagnosed, but not annotated in detail. Unfortunately, these algorithms are not good enough at predicting where the abnormalities are located, which is important for diagnosis and prognosis of disease. This limits the application of these algorithms in research and in clinical practice. I propose to use the "wisdom of the crowds"-internet users without specific expertise-to improve the predictions of the algorithms. While the crowd does not have experience with medical imaging, recent studies and pilot data I collected show they can still provide useful information about the images, for example by saying whether images are visually similar or not. Such information has not been leveraged before in medical imaging applications. I will validate these methods on three challenging detection tasks in chest computed tomography, histopathology images, and endoscopy video. Understanding how the crowd can contribute to applications that typically require expert knowledge will allow harnessing the potential of large unannotated sets of data, training more reliable algorithms, and ultimately paving the way towards using ML algorithms in clinical practice.

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“…One approach for dealing with 3D images is to select 2D parts of the original 3D images. For example, (Ørting et al, 2017;O'Neil et al, 2017) select axial slices. (Cheplygina et al, 2016) shows patches of 2D projections in various directions in the image.…”

Section: Type Of Imagementioning

confidence: 99%

A Survey of Crowdsourcing in Medical Image Analysis

Ørting¹,

Doyle²,

Hilten³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018 . We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

show abstract

Crowdsourced Emphysema Assessment

Cited by 6 publications

References 11 publications

A Survey of Crowdsourcing in Medical Image Analysis

A Survey of Crowdsourcing in Medical Image Analysis

CrowdDetective: Wisdom of the Crowds for Detecting Abnormalities in Medical Scans

A Survey of Crowdsourcing in Medical Image Analysis

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