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Statistical Analysis System (SAS)01:14

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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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SILA: a system for scientific image analysis.

Daniel Moreira1, João Phillipe Cardenuto2, Ruiting Shao3

  • 1Department of Computer Science, Loyola University Chicago, Chicago, IL, USA.

Scientific Reports
|November 1, 2022
PubMed
Summary
This summary is machine-generated.

Scientific image manipulation detection is crucial for research integrity. We present SILA, a novel human-in-the-loop system that analyzes PDFs, detects image alterations, and visualizes image relationships to aid peer review.

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Area of Science:

  • Scientific image analysis
  • Digital forensics
  • Scholarly publishing

Background:

  • Scientific images are frequently retouched or reused, with edits ranging from benign enhancements to misconduct.
  • Current automated technologies cannot reliably detect illegitimate image edits, necessitating human expert review.

Purpose of the Study:

  • To introduce SILA, a system designed to assist reviewers and editors in detecting scientific image manipulation.
  • To develop the first human-in-the-loop, end-to-end system for automated image analysis in scientific publications.

Main Methods:

  • SILA processes article PDFs to automatically extract figures.
  • It performs image manipulation detection and generates image provenance graphs to illustrate relationships.
  • A new dataset of scientific papers with annotated image manipulations and reuse was created for benchmarking.

Main Results:

  • The system demonstrated efficacy in identifying image manipulations and reuse.
  • Qualitative and quantitative results were obtained using the benchmark dataset.
  • SILA provides a principled way to integrate image analysis tools into the peer-review workflow.

Conclusions:

  • SILA offers a promising approach to support human experts in verifying the integrity of scientific images.
  • The developed dataset serves as a valuable benchmark for future research in this domain.
  • Automated image analysis tools, integrated with human oversight, can enhance the reliability of scientific publications.