Automated Detection of the Black Hole Sign for Patients with Intracerebral Hemorrhage Using Self-Supervised Learning
This study introduced a novel framework integrating SSL into medical image classification, particularly on BHS identification from head CT scans. The resulting pretrained head CT encoder model showed the potential to minimize manual annotation, which would significantly reduce labor, time, and costs. After fine-tuning, the framework demonstrated a promising performance for a specific downstream task, identifying the BHS to predict HE on comprehensive evaluation on diverse data sets. This approach holds promise for enhancing medical image analysis, particularly in scenarios with limited data availability.
Authors
- Julianne Murphy (ORCID: https://orcid.org/0000-0003-3430-024X)
- Hanyin Wang (ORCID: https://orcid.org/0000-0001-9884-9683)
- Andrea Zini (ORCID: https://orcid.org/0000-0003-1486-4507)
- Sebastiano Giacomozzi (ORCID: https://orcid.org/0000-0003-2859-670X)
- Andrew M. Naidech (ORCID: https://orcid.org/0000-0003-1065-5417)
- Laura Brancaleoni
- Jacinto C. Nascimento (ORCID: https://orcid.org/0000-0001-7468-5127)
- Meghan R. Hutch (ORCID: https://orcid.org/0000-0001-8130-9429)
- Ethan J. Houskamp (ORCID: https://orcid.org/0000-0002-2090-562X)
- Yuan Luo (ORCID: https://orcid.org/0000-0003-0195-7456)
- Tim Schwirtlich
Institutions
- Northwestern University (US)
- Istituto delle Scienze Neurologiche di Bologna (IT)
- University of Bologna (IT)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-05-07
- DOI
- https://doi.org/10.3174/ajnr.a8826
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00