Graph Conditioned Diffusion for Controllable Histopathology Image Generation
Department of Computing, Imperial College London, UK（帝国理工学院计算系）；Weill Cornell Medicine, NY, USA（韦尔·科恩医学中心）；Cornell Tech, NY, USA（康奈尔科技）；Centre for Inflammatory Disease, Imperial College London, London, UK（帝国理工学院炎症疾病研究中心）
Sarah Cechnicka, Matthew Baugh, Weitong Zhang, Mischa Dombrowski, Zhe Li, Johannes C. Paetzold, Candice Roufosse, Bernhard Kainz
Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sensitive areas such as medical imaging. Medical images feature inherent structure such as consistent spatial arrangement, shape or texture, all of which are critical for diagnosis. However, existing DPMs operate in noisy latent spaces that lack semantic structure and strong priors, making it difficult to ensure meaningful control over generated content. To address this, we propose graph-based object-level representations for Graph-Conditioned-Diffusion. Our approach generates graph nodes corresponding to each major structure in the image, encapsulating their individual features and relationships. These graph representations are processed by a transformer module and integrated into a diffusion model via the text-conditioning mechanism, enabling fine-grained control over generation. We evaluate this approach using a real-world histopathology use case, demonstrating that our generated data can reliably substitute for annotated patient data in downstream segmentation tasks. The code is available here.
MultiDiffSense: Diffusion-Based Multi-Modal Visuo-Tactile Image Generation Conditioned on Object Shape and Contact Pose
Department of Bioengineering, Imperial-X Initiative, Imperial College London（生物工程系、Imperial-X计划、帝国理工学院伦敦分校）；CAMS-Oxford Institute, University of Oxford（CAMS-牛津研究所、牛津大学）
Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation
Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院诊断放射学系）；Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院病理学系）；Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong（香港中文大学威尔斯亲王医院解剖及细胞病理学系）；Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong（香港城市大学赛马会动物医学及生命科学院传染病及公共卫生学系）；CityU Veterinary Diagnostic Laboratory Co., Ltd., City University of Hong Kong（香港城市大学城市大学兽医诊断实验室有限公司）
F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation
University of Utah（犹他大学）；The Catholic University of Korea College of Medicine（韩国天主教大学医学院）；Huntsman Cancer Institute（亨茨曼癌症研究所）；Scientific Computing and Imaging Institute（科学计算与成像研究所）
College of Artificial Intelligence and Automation, Hohai University（人工智能与自动化学院，河海大学）；College of DaYu, Hohai University（大禹学院，河海大学）；College of Water Conserwancy and Hydropower Engineering, Hohai University（水利水电工程学院，河海大学）；College of Computer Science and Technology, Zhejiang University of Technology（计算机科学与技术学院，浙江工业大学）
SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detection
Trauma Radiology AI Laboratory (TRAIL)（创伤放射AI实验室）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；University of Maryland School of Medicine（马里兰大学医学院）
Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（苏州生物医学工程与技术研究所，中国科学院）；SyCoIA, IMT Mines Ales（SyCoIA，IMT Mines Ales）
CoMoGen: COntrollable MOtion Dynamics and Interactions with Mask-Guided Video GENeration
Technical University of Munich（慕尼黑技术大学）；Max Planck Institute for Informatics, Saarland Informatics Campus（马克斯·普朗克信息研究所，萨尔兰信息校园）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Obsphera
