NASA · NASA-401 · record R0227

Pre-trained microscopy image neural network encoders

National Aeronautics and Space Administration · GRC: Glenn Research Center

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Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2022-09-19 (day precision)Source: 09/19/2022 00:00:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Convolutional Neural Network encoders were trained on over 100,000 microscopy images of materials.

Expected benefits

When deployed in downstream microscopy tasks through transfer learning, encoders pre-trained on MicroNet outperform ImageNet encoders. These pre-trained MicroNet encoders have been successfully deployed for semantic segmentation, instance segmentation, and regression tasks.

System outputs

Automatically segment microscopy features given limited annotated microscopy images.

Technology

AI classification: Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
System name(s): NEST (WCS) SSP
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: Not reported
Training and evaluation data: Annoted microscopy images of various materials (metals, composites, EBC/CMC, etc.)
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported