扩散模型新进展，针对眼底彩色图像提出序数建模，MICCAI 2026收录，医学影像生成必读。
arXiv:2602.24013v2 Announce Type: replace Abstract: Generative image models such as diffusion models can improve performance on clinically relevant ta…
医疗视觉问答也能“知道何时不懂”，置信度感知推理让AI诊断更可靠
arXiv:2608.10964v1 Announce Type: new Abstract: Reinforcement Fine-Tuning (RFT) has enabled medical Multimodal Large Language Models (MLLMs) to produc…
基于纵向多视角影像的乳腺癌风险预测模型，已被MICCAI 2026接收，医学AI前沿。
arXiv:2607.11343v1 Announce Type: cross Abstract: Accurate breast cancer risk prediction from screening mammography is critical for enabling personali…
未配对数据也能挖掘生物标志物？CALM提出可解释跨模态对齐新方法，入选MICCAI 2026。
arXiv:2607.01656v1 Announce Type: new Abstract: The interaction between brain structure and genetic influences is key to understanding neuropsychiatri…
用可验证解剖证据为医学多模态大模型装上“可信护栏”，感知-推理协同治理框架获MICCAI 2026早期接收（Top 9%）。
arXiv:2607.00060v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report gen…
语言引导医学图像分割遇照明干扰？对比Retinex学习让模型看清病灶，MICCAI 2026新思路。
arXiv:2606.27794v1 Announce Type: new Abstract: Language-guided Medical Image Segmentation (LMIS) has shown great potential to improve the delineation…
首个眼底图像像素级质量评估基准FunPiQ，精准定位病灶区域，助力医学影像AI落地。
arXiv:2606.25915v1 Announce Type: new Abstract: Color fundus photography (CFP) is the most common ophthalmic imaging modality for large-scale screenin…
用可证深度学习为灌注物理信息神经网络引入残差不确定性量化，让医学影像AI诊断更可靠。
arXiv:2603.09359v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have shown promise in addressing the ill-posed deconvolut…
医学影像遇上视觉大模型，Wasserstein对齐定位让分布外检测更可靠，MICCAI 2026前沿研究。
arXiv:2605.05161v2 Announce Type: replace Abstract: Zero-shot anomaly localisation via vision-language models (VLMs) offers a compelling approach for …
早录MICCAI 2026，提出统一多模态模型同步实现脑MRI数据补全与理解，打通医学影像分析新路径。
arXiv:2606.16484v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) hold great potential for medicine, as they inherit knowledg…