arXiv Computer Vision
Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models
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arXiv Computer Vision
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AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via $\alpha$-Corrected Binary Cross Entropy and Factorized Latent Supervision
AlphaRAD introduces a grounded zero‑shot classification framework for chest radiology that leverages structured medical concepts extracted from reports and a novel α‑Corrected Binary Cross‑Entropy loss to reduce in‑batch noise. It also presents FLaS, a lightweight cross‑modal fusion module that factorizes VLPM representations into independent subspaces, improving spatial grounding without adding parameters. The method achieves state‑of‑the‑art performance on 16 classification benchmarks and sets new records on several grounding, phrase‑grounding, and segmentation datasets.
arXiv AI
Jun 9
CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation
arXiv:2601. 15408v2 Announce Type: replace-cross Abstract: Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency.
arXiv AI
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CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training
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arXiv AI
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CONFLUX: A Latent Diusion Model for 3D Chest-CT Synthesis with RL Post-Training
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arXiv AI
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From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation
The paper introduces a 3D-CLIP encoder trained with structured hard negatives to improve vision‑language alignment for text‑to‑CT generation. This encoder drives a latent diffusion model that operates directly in 3D latent space, eliminating spatial artifacts from super‑resolution pipelines. Experiments on the CT‑RATE dataset show state‑of‑the‑art image fidelity and factual correctness across 18 pathological conditions, with lower inference time and GPU memory usage than competing methods.
arXiv AI
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From Alignment to Synthesis Contrastive Volumetric Grounding for Text-to-CT Generation
arXiv:2506. 00633v3 Announce Type: replace-cross Abstract: Generating semantically controllable 3D CT volumes from radiology reports requires more than a rich text encoder, it requires vision-language alignment grounded in volumetric space.