arXiv AI

Diffusion Model in Latent Space for Medical Image Segmentation Task

The paper introduces MedSegLatDiff, a diffusion-based framework that combines a variational autoencoder (VAE) with a latent diffusion model for medical image segmentation. By compressing images into a low-dimensional latent space, the method reduces noise and speeds up training, while a weighted cross‑entropy loss preserves tiny structures such as small nodules. Evaluated on ISIC‑2018, CVC‑Clinic, and LIDC‑IDRI datasets, MedSegLatDiff achieves state‑of‑the‑art Dice and IoU scores, generates diverse segmentation hypotheses, and produces confidence maps that enhance interpretability and reliability for clinical deployment.

arXiv Computer Vision

4d ago

MedDiME: Efficient Latent Diffusion with Adaptive Masking for Medical Counterfactual Generation

MedDiME is a latent-space, classifier‑guided diffusion framework designed for medical counterfactual image generation. It introduces a gradient‑driven adaptive masking mechanism that works directly in latent space, enabling spatially precise edits while avoiding the high computational and memory costs of pixel‑space methods. Experiments show MedDiME can produce high‑quality counterfactuals up to 40× faster and using 13× less GPU memory than previous diffusion baselines.

By Yan Zeng, Changlu Guo, Anders Nymark Christensen, Morten Rieger Hannemose, Anders Bjorholm Dahl

diffusion safety

arXiv Computer Vision

Aug 25

GET: Generative Embedding Translation for Medical Image Segmentation

arXiv:2608.22619v1 Announce Type: cross Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-t...

By Md Maklachur Rahman, Md Hasan Al Banna, Saraf Anjum, Mahmudul Hasan, Tracy Hammond

llms rag diffusion computer-vision

arXiv AI

Sep 10

Latent-to-Latent Flow for Volumetric Stochastic Segmentation

arXiv:2609.07460v1 Announce Type: cross Abstract: Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in...

By Omar Todd, Sooha Kim, Raghav Mehta, Katherine Mackay, David Bernstein, Alexandra Taylor, Fabio De Sousa Ribeiro, Ben Glocker

diffusion computer-vision

arXiv AI

Jul 8

Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation

arXiv:2603. 04024v2 Announce Type: replace-cross Abstract: Ambiguous 3D medical image segmentation often involves boundaries where different expert delineations are non-identical yet clinically plausible.

By Chao Wu, Mahesh Bhosale, Kangxian Xie, Pouya Karimian, David Doermann, Mingchen Gao

diffusion computer-vision safety

arXiv AI

Jun 26

Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis

arXiv:2606. 26764v1 Announce Type: cross Abstract: Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions.

By Yiheng Cao, Gustavo Andrade-Miranda, Jiatian Zhang, Lingxiao Zhao, Xin Gao

diffusion computer-vision safety

arXiv AI

Jun 26

MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation

arXiv:2606. 26712v1 Announce Type: cross Abstract: Skin lesion segmentation is a key task in computer-aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease classification.

By Jingjun Gu, Chaojie Shen, Yifeng Cao, Wei Zhang, Yiliu Li, Aobo Fan

diffusion computer-vision safety

arXiv AI

Sep 3

InstEditSeg: Instruction-Driven Image Editing for Polyp and Skin Lesion Segmentation

InstEditSeg is a generative framework that treats medical segmentation as an instruction-driven image editing task. Instead of producing binary masks, it renders a color-coded overlay on the original image guided by textual instructions, leveraging latent diffusion models to align with natural image distributions and reduce domain gaps. The method incorporates a DINOv3 visual encoder and a multi-scale feature pyramid fused into the diffusion U‑Net, and uses a dual‑branch classifier‑free guidance strategy to lower inference cost, achieving competitive accuracy on polyp and skin lesion datasets while improving cross‑domain generalization and multi‑lesion segmentation.

By Ziquan Liu, Zhewei Zhu, Xuyang Shi

diffusion computer-vision benchmarks

arXiv Machine Learning

Aug 27

Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models

The paper introduces an unsupervised approach to medical image segmentation by training a Denoising Diffusion Probabilistic Model (DDPM) on 21 unlabeled abdominal CT scans to learn anatomical features. The encoder weights from the DDPM are transferred to a U‑Net for downstream segmentation on the BTCV multi‑organ dataset, resulting in a significant Dice score improvement for liver segmentation from 0.75 to 0.93. In low‑data regimes, diffusion‑pretrained models retain robust performance, achieving high Dice scores even with only 10% of labeled data.

By Akshat G, Divyansh Gupta, Shaleen Bhatnagar, Shilpa Ankalaki, Tusar Kanti Mishra

diffusion computer-vision fine-tuning

arXiv AI

Aug 3

DualDiT: A Conditional Dual-Output Diffusion Transformer for Joint OCT Image and Segmentation Mask Generation

arXiv:2607. 29337v1 Announce Type: cross Abstract: Background and Objective: Generating realistic medical images with anatomically accurate segmentation masks helps address the shortage of annotated data in medical imaging, particularly in optical coherence tomography (OCT) of mouse eyes, where manual retinal layer delineation is labour-intensive due to tiny structures and required expertise, resulting in scarce datasets.

By Fernando Garc\'ia-Torres, Roc\'io del Amor, Sandra Morales, \'Alvaro Barroso, Peter Heiduschka, Bj\"orn Kemper, Valery Naranjo

llms diffusion computer-vision

Hugging Face Trending Papers

Aug 26

Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models

Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local texture patterns and lack awareness of global ana...

diffusion computer-vision fine-tuning

Hugging Face Trending Papers

Jun 25

Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis

Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions. To address this, we propose a 4D controllable generative framework for anatomically consistent data augmentation.

diffusion computer-vision 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

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