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Neural Networks in
Medical Imaging

J. Smith, A. Johnson, M. Williams

Abstract—

1. Introduction

[1]

2. Results

(t(30) = 1.5, p < 0.01)

Fig. 1

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Streamline peer review and ensure publication integrity with our comprehensive suite of tools. Detect potential issues before they reach publication.

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Verify your work before submission to ensure it meets the highest standards of integrity. Identify and address potential issues early.

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Proactive monitoring to safeguard your journal's reputation.

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Image duplication

Duplication detection in any image, within the same image or manuscript—or across science. Create your private library of images to compare against.

Statistical inconsistencies

Detect statistical inconsistencies in reporting. Up to 43 % more detectable cases than Statcheck

Missing citations

Identify claims that need a citation or citations that should not be there.

Journal monitoring

Continuously track whether your journal has systematic integrity risks, staying ahead of the curve.

Figure accessibility

Advanced figure accessibility issues detection: low contrast, color-blind violations, and readability issues.

Manipulation and Beautification

Detect inappropriate image beautification and manipulation in western blots and microscopy.

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Research

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Figure Reuse

Bioscience-scale automated detection of figure element reuse

This study presents an algorithm that detects figure element reuse in a large dataset of 760 thousand open access articles and 2 million figures, robust to various transformations. A panel review estimates that 0.6% of all articles contain fraudulent reuses, with inappropriate reuses occurring 43% across articles, 28% within an article, and 29% within a figure.

Missing Citation

Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models

This study develops an automated system using advanced neural networks to pinpoint sentences needing citations. It enhances document quality by identifying citation errors using a comprehensive dataset.

Figure Accessibility

A computational analysis of accessibility, readability, and explainability of figures in open access publications

This study develops computational techniques to assess the accessibility, readability, and explainability of figures in scientific publications. Our analysis reveals that approximately 20.6% of open access publications have issues related to these aspects, with detailed statistics provided for specific problems.

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