A multi-task self-supervised framework with spatial mix-up masking and contrastive predictive coding improves unsupervised domain adaptation for classifying multi-type point maps from cancer tissue regions.
Preparing medical imaging data for machine learning.Radiology, 295(1):4–15, 2020
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data
A multi-task self-supervised framework with spatial mix-up masking and contrastive predictive coding improves unsupervised domain adaptation for classifying multi-type point maps from cancer tissue regions.