A novel MIL architecture predicts zero-inflated beta parameters for TPS distributions in NSCLC using slide-level supervision.
Archives of Pathology & Laboratory Medicine148(7), 757–774 (Jul 2024)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC
A novel MIL architecture predicts zero-inflated beta parameters for TPS distributions in NSCLC using slide-level supervision.