2 papers
eess.IV2026
An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification
Chengkun Sun, Jinqian Pan, Renjie Liang +7
Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and trea…
stat.ME2025
Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift
Chao Ying, Jun Jin, Yi Guo +3
Collecting gold-standard phenotype data via manual extraction is typically labor-intensive and slow, whereas automated computational phenotypes (ACPs) offer a systematic and much f…