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Related Concept Videos

Papillary Dermis01:11

Papillary Dermis

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Dermis
The dermis might be considered the "core" of the integumentary system, as distinct from the epidermis and hypodermis. It contains blood and lymph vessels, nerves, and other structures, such as hair follicles and sweat glands. The dermis is made of two layers of connective tissue that comprise an interconnected mesh of elastin and collagenous fibers, produced by fibroblasts.
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Reticular Dermis01:15

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The papillary and reticular dermis are the two layers of the dermis. They are made of connective tissue with fibers of collagen extending from one to the other, making the border between the two somewhat indistinct. The dermal papillae extending into the epidermis belong to the papillary layer, whereas the dense collagen fiber bundles below belong to the reticular layer.
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Updated: Apr 28, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.

Chloe Cho1,2, Andrew J McNeil3,4, Bohan Jiang5

  • 1Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.

Proceedings of Spie--The International Society for Optical Engineering
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

Standardizing dermatologic imaging data with the Dermatology Imaging Data Structure (DermIDS) improves data quality and enables AI/ML research. This framework organizes diverse skin image datasets, revealing metadata gaps for better integration and scalability.

Keywords:
AI/ML-readydata managementdermatologic imagingharmonizationinteroperabilitymetadatamultimodal datascalable

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Data Science

Background:

  • Dermatologic imaging research is rapidly expanding, with over 70% of related PubMed articles published since 2016.
  • A lack of standardized infrastructure for organizing and describing non-protected health information (non-PHI) dermatologic imaging data hinders data integration, scalability, and interoperability.
  • Existing datasets are often inconsistently labeled and heterogeneous, impeding large-scale analysis and AI/ML development.

Purpose of the Study:

  • To propose and validate the Dermatology Imaging Data Structure (DermIDS), a novel framework for organizing dermatologic imaging data for non-PHI research.
  • To improve data quality, usability, and interoperability across diverse dermatologic imaging datasets.
  • To enable scalable, AI/ML-ready workflows in dermatologic imaging research.

Main Methods:

  • Developed the Dermatology Imaging Data Structure (DermIDS), inspired by the Brain Imaging Data Structure (BIDS).
  • Curated and processed 1,000,692 dermatologic images using the DermIDS framework.
  • Analyzed metadata features to identify gaps and assess the utility of DermIDS for standardization.

Main Results:

  • DermIDS supports multimodal photographic data (clinical, dermoscopy, 3D imaging) and organizes technical and clinical metadata.
  • Identified 1,256 unique metadata features across the processed images.
  • Revealed significant metadata gaps: 70% of clinical and 98% of technical metadata features were present in less than 100k images, highlighting inconsistencies.

Conclusions:

  • DermIDS provides a generalizable infrastructure for organizing dermatologic imaging data, enhancing usability and facilitating large-scale analysis.
  • The framework effectively reveals metadata inconsistencies and opportunities for standardization, crucial for advancing AI/ML in dermatology.
  • This work lays the foundation for harmonized, AI/ML-ready dermatologic imaging research.