Semantically Calibrated Evidence Composition for CT Vision-Language Learning

arXiv:2608.00239 · cs.CV · Submitted 2026-07-31 · Read on arXiv

cs.CV

Submitted: 2026-07-31

Updated: 2026-09-16

Terminology

Alignment — 21× in this paper · explained in 1 episode
Calibration — 21× in this paper · explained in 7 episodes
Grounding — 14× in this paper · explained in 3 episodes
AUC — 13× in this paper · explained in 2 episodes
Transformer — 10× in this paper · explained in 2 episodes
Representation Learning — 9× in this paper · explained in 2 episodes
AUROC — 7× in this paper · explained in 2 episodes
Consistency — 6× in this paper · explained in 3 episodes
Foundation Model — 6× in this paper · explained in 3 episodes
Linear Probing — 6× in this paper · explained in 2 episodes
Accuracy — 4× in this paper · explained in 1 episode
Foundation Models — 4× in this paper · explained in 7 episodes
Regularization — 4× in this paper · explained in 3 episodes
Ablation — 3× in this paper · explained in 1 episode
Aggregation — 3× in this paper · explained in 1 episode
Benchmark — 2× in this paper · explained in 2 episodes
Contrastive Learning — 2× in this paper · explained in 5 episodes
F1 Score — 1× in this paper · explained in 3 episodes
Image Local-Global Evidence Composition — 1× in this paper
Self-Supervised Learning — 1× in this paper · explained in 8 episodes
Semantic Alignment — 1× in this paper · explained in 3 episodes

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