arXiv Computer Vision
ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT
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arXiv Computer Vision
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DALE-CT: Depth-Aware 2D Slice Encoders Learn an Anatomical World Model of Chest CT
DALE-CT introduces depth‑aware 2D slice encoders that learn an anatomical world model of chest CT scans without 3D or positional supervision. By sampling self‑supervised views across a physical $z$‑axis slab, the encoder captures how anatomy changes between neighboring slices, enabling it to recover slice ordering and distinguish slices by anatomy alone. The model, trained on a large 287k‑scan corpus, achieves state‑of‑the‑art performance on CT‑RATE and is released with full code and evaluation tools.
Hugging Face Trending Papers
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Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography
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arXiv AI
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Anatomy Contextualized Adaption of CT Foundation Models
arXiv:2607. 27154v1 Announce Type: cross Abstract: CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals.
arXiv Machine Learning
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A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
arXiv:2606. 16991v1 Announce Type: cross Abstract: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload.
arXiv AI
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arXiv AI
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