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sezginer

  •  Türkiye
  •  Istanbul Medipol University
  •  International School of Medicine
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  • Member for 1 month, 3 weeks

Activity Overview

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Task 1: Radiology Report Generation
Challenge User

Participants build vision‑language models that translate a complete 3‑D chest CT scan into a free‑text radiology report covering findings and impression; performance is assessed with standard NLG scores (BLEU, METEOR, ROUGE‑L) plus the clinically aware CRG metric, using the CT‑RATE dataset split into public train/validation and hidden internal+external test sets.

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Task 4: Text-Conditional CT Generation
Challenge User

Without voxel‑level labels during training, systems must localize five key pathologies—pericardial effusion, pleural effusion, consolidation, lung opacity, and lung nodule—producing 3‑D heat‑maps that are scored on Dice, IoU, Hausdorff‑95, and Sensitivity against expert masks for 2000 hidden test scans.