SEW combines a thumbnail-level graph classifier with a focus predictor and local detail branch to classify whole-slide pathology images quickly, and uses clustered features to propose new prognostic tumor markers.
Hundredfold accelerating for pathological images diagnosis and prognosis through self-reform critical region focusing
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
SEW: Self-calibration Enhanced Whole Slide Pathology Image Analysis
SEW combines a thumbnail-level graph classifier with a focus predictor and local detail branch to classify whole-slide pathology images quickly, and uses clustered features to propose new prognostic tumor markers.