ProSona：用于多专家医学图像分割的提示引导个性化

医学图像中的任意分割

Segment Anything in Medical Images

MedSAM是一个基于157万图像-掩码对训练、覆盖10种模态和30余种癌症的通用医学图像分割基础模型，在86个内部和60个外部验证任务上优于模态专用模型。

谐化特征条件与频率提示个性化用于多评分者医学分割

Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation

本文提出谐化概率框架，通过自适应特征条件和频域个性化分离成像伪影与标注者差异，提升多评分者医学分割的准确性和可解释性。

用于皮肤病变分类中高效标注策略的联邦主动学习框架

Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification

针对联邦医疗场景标注成本高的问题，提出FedAL框架，通过本地与全局模型集成及集成熵主动选择标注样本，仅用50%数据即达到接近全数据FL的最先进皮肤病变分类性能。

Dynamic voting in multi-view learning for radiomics applications

Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radiomics is a recent medical imaging field that has shown during the past few years to be promising for achieving this personalization. However, a recent study shows that most of the state-of-the-art works in Radiomics fail to identify this problem as a multi-view learning task and that multi-view learning techniques are generally more efficient. In this work, we propose to further investigate the potential of one famil…

CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model. Despite the recent progress, it remains challenging to deal with heterogeneous data clients, as the discrepant data distributions usually prevent the global model from delivering good generalization ability on each participating client. In this paper, we propose CD^2-pFed, a novel Cyclic Distillation-guided Channel Decoupling framework, to personalize the global model in FL, under various settings of data heterogeneity. Different from previous works which est…

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and cli…

用于脑肿瘤分割中聚类联邦个性化的全脑影像组学

Whole-brain radiomics for clustered federated personalization in brain tumor segmentation

针对联邦医学图像分割中机构间与机构内特征偏移导致的异质性问题，提出基于全脑影像组学特征聚类的新型个性化算法，通过聚类去中心化数据集微调全局模型，在FeTS2022数据集上验证了有效性。

具有残差信息鱼尔矩阵的个性化联邦学习用于医学图像分割

Personalized Federated Learning with Residual Fisher Information for Medical Image Segmentation

本文提出pFL-ResFIM框架，通过残差信息鱼尔矩阵量化模型参数对领域差异的敏感性，实现客户端自适应个性化，在公共数据集上验证了其有效性。

FL-MedSegBench: 一个全面的联邦学习在医学图像分割中的基准测试

FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation

FL-MedSegBench是一个全面的医学图像分割联邦学习基准测试，评估了多种FL方法在分割精度、公平性、通信效率等多维度的表现，揭示了个性化方法在特定场景下的优势。

多样化与个性化的多评分者医学图像分割

Diversified and Personalized Multi-rater Medical Image Segmentation

针对多评分者医学图像分割中标注模糊的问题，提出D-Persona两阶段框架，通过Probabilistic U-Net与注意力投影头同时实现多样化和个性化分割，达到SOTA性能。