SynerMedGen is a unified framework that aligns medical multimodal understanding with generation tasks through task alignment. It introduces three generation‑aligned understanding tasks and a two‑stage training strategy that transfers representations learned during understanding to medical image synthesis. The model achieves strong zero‑shot performance on 22 synthesis tasks and outperforms state‑of‑the‑art specialized and unified models when combined with generation training, supported by a new 1M‑sample SynerMed dataset.
By Weiren Zhao, Yi Dong, Cheng Chen
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
arXiv:2607. 03103v1 Announce Type: cross Abstract: Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding knowledge sharing across anatomically related tasks.
By Jiahao Liu, Hang Wei, Shuai Wu
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
MedGEN-Bench is a new benchmark for open‑ended multimodal medical generation that addresses limitations in current medical visual benchmarks, such as query‑image misalignment, closed‑ended answer spaces, and text‑centric outputs. The dataset contains 6,422 image‑text pairs across six imaging modalities, 15 clinical tasks, and 27 subtasks, including VQA, image editing, and contextual multimodal generation pairs. Evaluation combines reference‑based fidelity metrics with a structured, checklist‑guided assessment by a medical VLM judge, and preliminary results show that image‑output tasks remain unsaturated while contextual augmentation improves image‑instruction similarity.
By Junjie Yang, Yuhao Yan, Gang Wu, Rui Qian, Zhisheng Chen, Haijiang Li, Yuhe Wu, Qichao Zhao, Dawen Tian, Xiang Wan, Fenglei Fan, Wenjian Qin, Yongquan Zhang, Feiwei Qin, Changmiao Wang
arXiv:2608. 07340v1 Announce Type: cross Abstract: Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through registration.
By Jia Wang, Jiaming Cai, Zunying Hu, Zhanjie Wu, Jinyuan Liu, Hua Cheng, Yun Peng
arXiv:2608.28923v1 Announce Type: cross
Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...
By Noah Videcrantz, Mostafa Mehdipour Ghazi
Due to the scarcity of expert-annotated data, Semi-Supervised Medical Image Segmentation (SSMIS) has emerged as a promising approach. Many anatomical structures in medical images exhibit significant intra-class heterogeneity, with different regions showing heterogeneous intensity patterns within the same structure.
arXiv:2603. 16551v2 Announce Type: replace-cross Abstract: Generative models are increasingly used to augment medical imaging datasets for fairer AI, yet a key assumption often goes unexamined: that generators produce equally high-quality images across demographic groups.
By Mahmoud Ibrahim, Bart Elen, Chang Sun, Gokhan Ertaylan, Michel Dumontier
arXiv:2605.05522v3 Announce Type: replace-cross
Abstract: Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities s...
By Aneesh Rangnekar, Joao Miranda, Natally Horvat, Stephanie Chahwan, Samir Alrayess, Aditya Apte, Aditi Iyer, Eve LoCastro, Revathi Ravella, Marc J Gollub, Iva Petkovska, Jesse Joshua Smith, Paul Romesser, Julio Garcia-Aguilar, Harini Veeraraghavan, Joseph O Deasy
The paper introduces PRISM, a Compositional Reward Model framework that decomposes image quality into multiple verifier‑grounded stages for conditional medical image generation. By assigning distinct rewards for fine‑to‑coarse properties—such as intensity, texture, structural alignment, and semantic fidelity—and combining them via a Hierarchical Constrained Propagation mechanism, PRISM addresses shortcomings of single‑scalar reward approaches. Experiments on PanNuke, CeDeM, and ISIC datasets show that data generated with PRISM improves downstream model performance, achieving higher mDice, lower MRE, and increased F1 scores compared to baseline methods.
By Aayush Kumar Tyagi, Prathosh A. P., Mausam
arXiv:2608. 15516v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated strong performance in multimodal understanding and generation.
By Pengyu Wang, Baochen Xiong, Xiaoshan Yang, Yifan Xu, Zhang Qimeng, Haifeng Chen, Changsheng Xu