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CAMEL introduces a cross-architecture distillation framework with an asymmetric masking strategy that transfers knowledge from large retinal foundation models like RETFound to ultra-lightweight student models, preserving accuracy while drastically reducing computational cost for resource-limited clinical deployment.
ReliMorph 提出一个可靠性引导的对称框架，将局部对应评估与空间专家路由及残差速度门控耦合，用于 3D 可变形医学图像配准，并在单模态和多模态任务上超越现有方法。
Meta-CLIP introduces a metadata-conditioned contrastive learning framework that reweights cross-modal negatives using DICOM acquisition fields, improving zero-shot disease classification on CT-RATE and several CXR benchmarks over CT-CLIP.
EviGround-CT proposes sentence-to-volume grounding to verify LLM-generated chest CT reports by predicting 3D evidence masks for each positive finding sentence, improving evidence-supported precision from 0.263 to 0.365 over category conditioning. The work is evaluated on CT-RATE and a ReXGroundingCT subset to detect unsupported findings.
OpenRadView is a free, open-source desktop viewer for personal CT, MRI and X-ray scans, supporting DICOM and NIfTI files with linked axial/coronal/sagittal views, experimental 3D and measurements, plus optional MCP access for AI agents.
该论文提出 RadCarve 方法，将放射学报告的生成单元直接定义为解剖区域集合，通过组成特化的查询库联合预测单元存在与区域构成，在 Chest ImaGenome 和 PadChest-GR 上以不到大模型六分之一的参数量取得领先的报告生成性能。
TernScope presents a full-network ternary segmentation model (0.2483M params, ternary weights in all 25 convolutions) co-designed with a matching hardware accelerator, achieving 88.46% lower compute-array area than INT8 Gemmini at 45nm while performing well on ClinicDB and ColonDB without fine-tuning.
MoSAIK 从标准多线圈 TSE k-space 中用 Transformer 直接估计刚体运动参数，并将其用于模型化的运动感知重建，从而在无需外部导航器的情况下快速回顾性校正脑 MRI 运动伪影。
MIT, Boston Children's Hospital and Inria researchers presented E3-Pose, a deep learning model with baked-in left-right symmetry physics for tracking fetal head motion, aiming to produce reliable high-resolution fetal brain MRI for prenatal diagnosis.
CF-NODE 提出了一种反事实空间节点干预的测试时间自适应框架，用于跨域糖尿病视网膜病变分级，在五个外部数据集上显著优于 TENT、EATA、SAR 和 T3A 等方法。
作者展示了使用 Claude Opus 5.5 在数小时内设计出一套约 3.1 万美元的开源低场头部 MRI 扫描仪的概念验证方案（仅仿真，未实际制造）。该设计基于 60 mT NdFeB Halbach 阵列，可从普通插座供电，无需低温冷却或屏蔽室，以四个模块形式运输。
该论文提出了一种名为 Conformal Adaptation 的框架，通过共形风险控制（conformal risk control）在低分辨率下认证预测信号，再对已认证区域进行精细化计算，从而在 3D 医学图像分割、星系流量预测和钙信号预测等任务上实现 2.5–72 倍的墙钟加速，同时保留不确定性估计。
VascuSR 是一个迭代式解剖感知扩散框架，将显式3D血管引导与CT超分辨率结合，以提升重建的解剖准确性与血管连通性，在MSD和HVM数据集上显著优于现有基线。
该论文提出一种泄漏可控的多模态多任务学习框架，结合SFCN MRI编码器、因果临床Transformer和ODE-GRU动力学，用于阿尔茨海默病诊断与病程进展预测，在ADNI、OASIS-2、MIRIAD数据集上取得内部诊断AUROC 0.935、混合AUROC 0.951，并在OASIS-3上完成外部验证。
BigCell是首个能在原生千兆像素分辨率下生成连贯全切片病理图像的方法，采用流匹配Transformer在2048×2048像素上训练超过12万张公开WSI，可生成高达32,768×32,768像素的图像，并通过合成样本将少样本切片级分类性能提升最多18%。
该论文提出了一种基于几何的有限特征联想记忆容量理论，将检索干扰分解为有限特征噪声与结构性干扰，并推导出与表示几何直接挂钩的容量上限预测，随后在合成、视觉和医学图像表示上进行了验证。
NVIDIA Inception startups are building AI applications to address breast cancer care gaps, from FDA-cleared imaging systems like iSono Health's wearable 3D ultrasound ATUSA to density-assessment software and faster genomic assay tools, all powered by NVIDIA AI infrastructure.
The paper introduces Generalized Biomedicine Discovery (GBD), a unified benchmark for open-world medical imaging that handles long-tailed rare diseases, subtle lesions, and hierarchical taxonomies, and proposes SCAN, a cognition-inspired method using predictive suppression, surprise-evoked saliency, and complementary accommodation to improve novel concept discovery while preserving known clinical knowledge.
OpenMed Spine Lab shows how a 10-pixel shift in a spine X-ray landmark changes the measured L4-L5 angle, and Liquid AI's LFM2.5-VL-3B model locally re-explains the new values in under a second.
RECESSIM's wiki documents the successful reverse engineering of the GE Flashpad wireless digital X-ray detector, enabling full 2048x2048 image readout over Ethernet without the original Optima console, firmware modification, or proprietary software.