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Lossy DICOM conversion may affect AI performance.

Robin Sebastian Mayer1, Fabian Fliedner1, Ingvild Frøberg Mathisen1

  • 1Dr. Senckenberg Institutes of Pathology and Human Genetics, Goethe-University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany.

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|July 8, 2025
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Summary
This summary is machine-generated.

Digitizing pathology slides for long-term storage in DICOM format is crucial. While converted images appear similar, they are not identical, impacting AI model performance and requiring re-evaluation for diagnostic use.

Keywords:
Adversarial exampleComputational pathologyDICOMDigital pathologyImage compression

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Area of Science:

  • Digital pathology
  • Medical imaging standards
  • Artificial intelligence in healthcare

Background:

  • Pathology slides are increasingly digitized for accessibility.
  • Storing digitized slides in the DICOM format ensures long-term accessibility.
  • Current digitization workflows often involve vendor-specific formats and conversions to DICOM, raising concerns about data integrity.

Purpose of the Study:

  • To investigate the consequences of converting MRXS pathology slide images to DICOM format.
  • To assess the impact of DICOM conversion on image data and subsequent AI model performance.
  • To evaluate the suitability of DICOM for diagnostic use and future interoperability.

Main Methods:

  • MRXS files of bladder, ovarian, and prostate tissue were converted to DICOM using a vendor converter and an open-source tool with JPEG re-compression.
  • Human visual inspection and Structure Similarity Index (SSIM) were used to compare original and converted images.
  • Artificial intelligence (AI) models, including Convolutional Neural Networks (CNNs) and foundation models, were employed to differentiate image formats and evaluate performance changes.

Main Results:

  • No human-perceptible differences were observed between original and converted DICOM images.
  • Converted images were not identical to originals, with average SSIM scores ranging from 0.85 to 0.96.
  • AI models could distinguish between original and converted images with up to 99.5% accuracy.
  • Significant AI performance differences were noted in 5 out of 64 scenarios, particularly with limited training data.
  • Vendor-specific converters generally achieved higher SSIM values.

Conclusions:

  • DICOM conversion of pathology slides is not lossless, and converted images are not identical to originals.
  • All diagnostic processes and AI algorithms must be re-evaluated using DICOM-converted files.
  • Despite conversion differences, the DICOM format offers excellent interoperability for future applications.
  • Initial AI training with converted files did not consistently show decreased performance, suggesting potential for future use.