arXiv Computer Vision
Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models
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arXiv Computer Vision
Sep 3
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
Jul 8
CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training
arXiv:2607. 02998v2 Announce Type: replace-cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.
arXiv AI
Jul 7
CONFLUX: A Latent Diusion Model for 3D Chest-CT Synthesis with RL Post-Training
arXiv:2607. 02998v1 Announce Type: cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.
arXiv AI
Sep 17
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 Machine Learning
Oct 22
Structure is Supervision: Multiview Masked Autoencoders for Radiology
The paper introduces Multiview Masked Autoencoder (MVMAE), a self‑supervised framework that uses the natural multi‑view structure of radiology studies to learn view‑invariant, disease‑relevant representations by combining masked image reconstruction with cross‑view alignment. An extended version, MVMAE‑V2T, adds radiology reports as an auxiliary text signal to improve semantic grounding while keeping inference vision‑only. Experiments on MIMIC‑CXR, CheXpert, and PadChest show that MVMAE outperforms supervised and vision‑language baselines, and MVMAE‑V2T yields further gains, especially when labeled data are scarce.