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Data · dataset · 2026

User-Guided Interactive Machine Learning for High-Throughput, Multi-Scale Helium Bubble Segmentation and Quantification

Listed in ScienceDB

This project introduces a novel User-Guided Interactive Machine Learning Framework for high-throughput, multi-scale helium bubble segmentation and quantification.

Description

Combining advanced image processing techniques with deep learning, the framework leverages Mask R-CNN for semantic segmentation and incorporates GCM for refined analysis. This tool provides a scalable and customizable approach for helium bubble analysis, empowering researchers with precise, user-friendly, and interactive workflows.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Image 75% · Nanotechnology 72%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
ScienceDB10.57760/sciencedb.j00186.008938 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[field].anzsrc:group:4018enrichment · scidb cntaxonomy-embedding@1.0.0title+keywords+description (72%)
concepts[field].local:field:earth-environmentalmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:engineeringmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:humanitiesmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:life-sciencesmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:social-sciencemapping · scidb cnconnector:scidb_cn@1.0.0
concepts[modality].local:modality:imageenrichment · scidb cnkeyword-concept-rules@1.0.0title+description (75%)
descriptionsource · scidb cnconnector:scidb_cn@1.0.0/metadata/dc/description
license_textsource · scidb cnconnector:scidb_cn@1.0.0
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