CLIP Architecture for Abdominal CT Image-Text Alignment and Zero-Shot Learning: Investigating Batch Composition and Data Scaling
Vision-language models trained with contrastive learning on paired medical images and reports show strong zero-shot diagnostic capabilities, yet the effect of training batch composition on learned representations remains unexplored for 3D medical imaging. We reproduce Merlin, a dual-encoder model that aligns 3D abdominal CT volumes with radiology reports using symmetric InfoNCE loss, achieving a zero-shot macro F1 of 74.45% across 30 findings (original: 73.00%). We then investigate two axes of v
Record details
Published: 15 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (15 April 2026), “CLIP Architecture for Abdominal CT Image-Text Alignment and Zero-Shot Learning: Investigating Batch Composition and Data Scaling,” evidence record 5840, https://ethics.ai/record/5840 (originally published by arXiv).
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