Compi Hub: A Public Repository for Sharing and Discovering Compi Pipelines

Nogueira-Rodríguez, Alba; López-Fernández, Hugo; Graña‐Castro, Osvaldo; Reboiro-Jato, Miguel; Glez-Peña, Daniel

doi:10.1007/978-3-030-54568-0_6

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…Compi can be seen as an ecosystem comprising: (i) compi , the workflow engine with a command-line user interface to control the pipeline execution, (ii) compi-dk , a command-line tool to assist in the development and packaging of Compi-based applications, and (iii) Compi Hub, a public repository of Compi pipelines that allows users to easily discover, navigate and reuse them ( Nogueira-Rodríguez et al, 2021 ). Figure 1 illustrates the development and deployment lifecycle of Compi-based applications.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, Compi also incorporates several features that meet the needs of the most advanced users, such as support for multiple programming languages or advanced management and control of workflow execution. In this sense, Compi is more than a workflow engine, it was created as an application framework for developing pipeline-based end-user applications by providing: (i) automatic user interface generation—generates a classical command-line interface (CLI) for the entire pipeline based on its parameter specifications; (ii) application packaging—provides a version-control-friendly mechanism to package the pipeline application and its dependencies into a Docker image; and (iii) application distribution, supported by Compi Hub ( Nogueira-Rodríguez et al, 2021 )—a public repository where researchers can easily and freely publish their Compi pipelines and related documentation, making them available for other researchers.…”

Section: Introductionmentioning

confidence: 99%

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Compi: a framework for portable and reproducible pipelines

López-Fernández

Graña‐Castro

Nogueira-Rodríguez

et al. 2021

PeerJ Computer Science

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Compi is an application framework to develop end-user, pipeline-based applications with a primary emphasis on: (i) user interface generation, by automatically generating a command-line interface based on the pipeline specific parameter definitions; (ii) application packaging, with compi-dk, which is a version-control-friendly tool to package the pipeline application and its dependencies into a Docker image; and (iii) application distribution provided through a public repository of Compi pipelines, named Compi Hub, which allows users to discover, browse and reuse them easily. By addressing these three aspects, Compi goes beyond traditional workflow engines, having been specially designed for researchers who want to take advantage of common workflow engine features (such as automatic job scheduling or logging, among others) while keeping the simplicity and readability of shell scripts without the need to learn a new programming language. Here we discuss the design of various pipelines developed with Compi to describe its main functionalities, as well as to highlight the similarities and differences with similar tools that are available. An open-source distribution under the Apache 2.0 License is available from GitHub (available at https://github.com/sing-group/compi). Documentation and installers are available from https://www.sing-group.org/compi. A specific repository for Compi pipelines is available from Compi Hub (available at https://www.sing-group.org/compihub.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compi: a framework for portable and reproducible pipelines

López-Fernández

Graña‐Castro

Nogueira-Rodríguez

et al. 2021

PeerJ Computer Science

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“…The polyp detection system here presented was developed and evaluated using a private dataset, which is comparable in size to other private datasets used in similar studies [66]. Remarkably, this dataset includes both WL and NBI polyp-images, normal-mucosa images, and, unlike other public datasets for polyp detection, it includes additional information regarding polyp histology, morphology and size.…”

Section: Discussionmentioning

confidence: 99%

“…In this work, pipelines used for model development and evaluation were implemented with Compi [65]. Compi is a framework for the development of pipeline-based, command-line applications, which provides developers with several useful features, such as the automatic generation of the user interface, the packaging of applications together with their dependencies in Docker images, or an easy way to distribute applications through the public Compi Hub repository [66].…”

Section: Pipeline Developmentmentioning

confidence: 99%

Real-time polyp detection model using convolutional neural networks

Nogueira-Rodríguez

Domínguez-Carbajales

Campos-Tato

et al. 2021

Neural Comput & Applic

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Colorectal cancer is a major health problem, where advances towards computer-aided diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement. Here, a deep learning model for real-time polyp detection based on a pre-trained YOLOv3 (You Only Look Once) architecture and complemented with a post-processing step based on an object-tracking algorithm to reduce false positives is reported. The base YOLOv3 network was fine-tuned using a dataset composed of 28,576 images labelled with locations of 941 polyps that will be made public soon. In a frame-based evaluation using isolated images containing polyps, a general F1 score of 0.88 was achieved (recall = 0.87, precision = 0.89), with lower predictive performance in flat polyps, but higher for sessile, and pedunculated morphologies, as well as with the usage of narrow band imaging, whereas polyp size < 5 mm does not seem to have significant impact. In a polyp-based evaluation using polyp and normal mucosa videos, with a positive criterion defined as the presence of at least one 50-frames-length (window size) segment with a ratio of 75% of frames with predicted bounding boxes (frames positivity), 72.61% of sensitivity (95% CI 68.99–75.95) and 83.04% of specificity (95% CI 76.70–87.92) were achieved (Youden = 0.55, diagnostic odds ratio (DOR) = 12.98). When the positive criterion is less stringent (window size = 25, frames positivity = 50%), sensitivity reaches around 90% (sensitivity = 89.91%, 95% CI 87.20–91.94; specificity = 54.97%, 95% CI 47.49–62.24; Youden = 0.45; DOR = 10.76). The object-tracking algorithm has demonstrated a significant improvement in specificity whereas maintaining sensitivity, as well as a marginal impact on computational performance. These results suggest that the model could be effectively integrated into a CAD system.

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“…Compi uses the markup language XML to define the workflows as the creators López-Fernández et al argue that a DSL for defining workflows is "less interoperable, being difficult to produce or consume from languages other than the one on which the DSL is based", and that XML is "easy to validate syntactically and semantically through schemas" [74]. A repository of Compi workflows is available through the Compi hub project 18 which aims to provide community exploration of the workflow, including automatic visualisation of the workflow tasks and links to sample input data [84].…”

Section: Markup or Domain-specific Languagementioning

confidence: 99%

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Oakes¹,

Famelis²,

Sahraoui³

2022

Preprint

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Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the "route" of options that a domain expert may choose to take while developing their solution.To ground our conceptual framework in the stateof-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation.The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

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Compi Hub: A Public Repository for Sharing and Discovering Compi Pipelines

Cited by 8 publications

References 13 publications

Compi: a framework for portable and reproducible pipelines

Compi: a framework for portable and reproducible pipelines

Real-time polyp detection model using convolutional neural networks

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

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