Towards Noise-Resilient Few-Shot Learning: Optimizing Prototypes for Glioblastoma Classification
Individual author or multiple authors
Group
Major
Computer Science (CS)
Category of Work
Comps
Additional Category of Work
None
Degree
Bachelor of Arts
Class Year
2025
Comps Adviser(s)
Rafferty, Anna
Keywords
Few-shot Learning, Noise- Resilient Machine Learning, Glioblastoma Multiforme, Medical Imaging, Sampling Optimization.
Academic Civic Engagement (ACE) Comps Designation
no
Format
application/pdf
Files Uploaded
Text (paper)
Rights Management
Student author/s retain copyright to this work. Through online submission process, author/s granted Carleton College the non-exclusive rights to preserve this work as part of Carleton's academic history and to use it for teaching purposes and/or institutional research and assessment.
