Hugging Face Trending Papers July 15, 2026

TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model

Read the original on Hugging Face Trending Papers →

We introduce TCAM-Diff, a novel 3D medical image generation model that reduces the memory requirements to encode and generate high-resolution 3D data. This model utilizes a decoder-only autoencoder method to learn triplane representation from dense volume and leverages generalization operations to prevent overfitting.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI

Jun 17

Recover Semantics First, Generate Better: Improved Latent Modeling for 3D MRI Reconstruction and Cross-Contrast Synthesis

arXiv:2606. 17989v1 Announce Type: cross Abstract: Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis.

By Yonghao Chen, Sicheng Yang, Rui Tang, Lei Zhu

diffusion

arXiv AI

Aug 11

Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching

arXiv:2608. 08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task.

By Daniele Molino, Alessio Zoboli, Camillo Maria Caruso, Valerio Guarrasi, Paolo Soda

diffusion multimodal

arXiv AI

Aug 11

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.

By Daniele Molino, Camillo Maria Caruso, Filippo Ruffini, Paolo Soda, Valerio Guarrasi

diffusion multimodal benchmarks safety

arXiv AI

Jun 19

BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation

arXiv:2606. 19651v1 Announce Type: new Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing.

By Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms rag diffusion nlp benchmarks safety

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.

By Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms diffusion reinforcement-learning

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.

By Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms diffusion reinforcement-learning

Related stories

Recover Semantics First, Generate Better: Improved Latent Modeling for 3D MRI Reconstruction and Cross-Contrast Synthesis

Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching

From Alignment to Synthesis Contrastive Volumetric Grounding for Text-to-CT Generation

BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation

CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training

CONFLUX: A Latent Diusion Model for 3D Chest-CT Synthesis with RL Post-Training