Anatomy-guided prompting with cross-modal self-alignment for whole-body PET-CT breast cancer segmentation [0.03%]
基于解剖结构的跨模态自对齐乳腺癌分割Prompt方法研究：PET/CT全身应用初探
Jiaju Huang,Xiao Yang,Xinglong Liang et al.
Jiaju Huang et al.
Accurate segmentation of breast cancer in PET-CT images is crucial for precise staging, monitoring treatment response, and guiding personalized therapy. However, the small size and dispersed nature of metastatic lesions, coupled with the sc...
MIL-Adapter: Coupling multiple instance learning and vision-language adapters for few-shot slide-level classification [0.03%]
MIL-Adapter：结合多示例学习和视觉语言适配器进行少量样本的幻灯片级别分类
Pablo Meseguer,Rocío Del Amor,Valery Naranjo
Pablo Meseguer
Contrastive language-image pretraining has greatly enhanced visual representation learning and enabled zero-shot classification. Vision-language language models (VLM) have succeeded in few-shot learning by leveraging adaptation modules fine...
Robust non-rigid image-to-patient registration for contactless dynamic thoracic tumor localization using recursive deformable diffusion models [0.03%]
基于递归可变形扩散模型的无接触动态胸腔肿瘤定位的鲁棒非刚性图像到病人配准方法
Dongyuan Li,Yixin Shan,Yuxuan Mao et al.
Dongyuan Li et al.
Deformable image-to-patient registration is essential for surgical navigation and medical imaging, yet real-time computation of spatial transformations across modalities remains a major clinical challenge-often being time-consuming, error-p...
Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets [0.03%]
弗雷歇放射组学距离（FRD）：一种比较医学影像数据集的通用度量标准
Nicholas Konz,Richard Osuala,Preeti Verma et al.
Nicholas Konz et al.
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Cu...
Generating synthetic MRI scans for improving Alzheimer's disease diagnosis [0.03%]
生成用于改善阿尔茨海默病诊断的合成MRI图像
Rosanna Turrisi,Giuseppe Patané
Rosanna Turrisi
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Magnetic Resonance Imaging (MRI) combined with Machine Learning (ML) enables early diagnosis, but ML models often underperform when trai...
Marianne Rakic,Andrew Hoopes,Mazdak S Abulnaga et al.
Marianne Rakic et al.
Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commonly used in medical image analysis for population studies an...
Knowledge-guided multi-geometric window transformer for cardiac cine MRI reconstruction [0.03%]
知识引导的多几何窗口变压器心脏电影MRI重建方法
Jun Lyu,Guangming Wang,Yunqi Wang et al.
Jun Lyu et al.
Magnetic resonance imaging (MRI) plays a crucial role in clinical diagnosis, yet traditional MR image acquisition often requires a prolonged duration, potentially causing patient discomfort and image artifacts. Faster and more accurate imag...
IUGC: A benchmark of landmark detection in end-to-end intrapartum ultrasound biometry [0.03%]
IUGC：端到端产时超声生物测量标志检测的基准测试
Jieyun Bai,Yitong Tang,Xiao Liu et al.
Jieyun Bai et al.
Accurate intrapartum biometry plays a crucial role in monitoring labor progression and preventing complications. However, its clinical application is limited by challenges such as the difficulty in identifying anatomical landmarks and the v...
Hippocampal surface morphological variation-based genome-wide association analysis network for biomarker detection of Alzheimer's disease [0.03%]
基于海马体形态变异的全基因组阿尔茨海默病生物标志物检测关联分析网络
Xiumei Chen,Xinyue Zhang,Wei Xiong et al.
Xiumei Chen et al.
Performing genome-wide association analysis (GWAS) between hippocampus and whole-genome data can facilitate disease-related biomarker detection of Alzheimer's disease (AD). However, most existing studies have prioritized hippocampal volume ...
VHU-Net: Variational hadamard U-Net for body MRI bias field correction [0.03%]
变种Hadamard-U-Net在体MRI偏置场校正中的应用
Xin Zhu,Ahmet Enis Cetin,Gorkem Durak et al.
Xin Zhu et al.
Bias field artifacts in magnetic resonance imaging (MRI) scans introduce spatially smooth intensity inhomogeneities that degrade image quality and hinder downstream analysis. To address this challenge, we propose a novel variational Hadamar...