Evan Maestri
maestriev
- United States of America
- Stanford University
- Immunology
- Website
Statistics
- Member for 3 months, 2 weeks
- 5 challenge submissions
Activity Overview
Task 2: Multi-Abnormality Classification
Challenge UserGiven a volumetric chest CT, algorithms must output an 18‑length binary vector indicating the presence of common thoracic conditions (e.g., pleural effusion, lung nodule); evaluation on hidden test cohorts combines AUROC, F1, Precision, Recall, and Accuracy, with point‑based ranking driven by permutation‑test wins.
Task 3: Self-Supervised Multi-Abnormality Localization
Challenge UserParticipants synthesize anatomically plausible 3‑D chest CT volumes directly from radiology text prompts, aiming for high visual fidelity, realistic Hounsfield distributions, and tight semantic alignment; success is measured with CT‑adapted generative metrics (FVDI3D, FVDCT‑Net, CT‑CLIP, FID) and ranked via the same permutation‑based point system.
