pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation

Medical image segmentation is crucial for computer-aided diagnosis, yet privacy constraints hinder data sharing across institutions. Federated learning addresses this limitation, but existing approaches often rely on lightweight architectures that struggle with complex, heterogeneous data. Recently, the Segment Anything Model (SAM) has shown outstanding segmentation capabilities; however, its massive encoder poses significant challenges in federated settings. In this work, we present the first personalized federated SAM framework tailored for heterogeneous data scenarios in medical image segmentation. Our framework integrates two key innovations: (1) a personalized strategy that aggregates only the global parameters to capture cross-client commonalities while retaining the designed L-MoE (Localized Mixture-of-Experts) component to preserve domain-specific features; and (2) a decoupled global-local fine-tuning mechanism that leverages a teacher-student paradigm via knowledge distillation to bridge the gap between the global shared model and the personalized local models, thereby mitigating overgeneralization. Extensive experiments on two public datasets validate that our approach significantly improves segmentation performance, achieves robust cross-domain adaptation, and reduces communication overhead.

Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors

A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream manipulation. To address these limitations, we propose AffordDex, a novel framework with two-stage training that learns a universal grasping policy with an inherent understanding of both motion priors and object affordances. In the first stage, a trajectory imitator is pre-trained on a large corpus of human hand motions to instill a strong prior for natural movement. In the second stage, a residual module is trained to adapt these general human-like motions to specific object instances. This refinement is critically guided by two components: our Negative Affordance-aware Segmentation (NAA) module, which identifies functionally inappropriate contact regions, and a privileged teacher-student distillation process that ensures the final vision-based policy is highly successful. Extensive experiments demonstrate that AffordDex not only achieves universal dexterous grasping but also remains remarkably human-like in posture and functionally appropriate in contact location. As a result, AffordDex significantly outperforms state-of-the-art baselines across seen objects, unseen instances, and even entirely novel categories.

ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification

Precise classification of megakaryocytes is crucial for diagnosing myelodysplastic syndromes. Although self-supervised learning has shown promise in medical image analysis, its application to classifying megakaryocytes in stained slides faces three main challenges: (1) pervasive background noise that obscures cellular details, (2) a long-tailed distribution that limits data for rare subtypes, and (3) complex morphological variations leading to high intra-class variability. To address these issues, we propose the ActiveSSF framework, which integrates active learning with self-supervised pretraining. Specifically, our approach employs Gaussian filtering combined with K-means clustering and HSV analysis (augmented by clinical prior knowledge) for accurate region-of-interest extraction; an adaptive sample selection mechanism that dynamically adjusts similarity thresholds to mitigate class imbalance; and prototype clustering on labeled samples to overcome morphological complexity. Experimental results on clinical megakaryocyte datasets demonstrate that ActiveSSF not only achieves state-of-the-art performance but also significantly improves recognition accuracy for rare subtypes. Moreover, the integration of these advanced techniques further underscores the practical potential of ActiveSSF in clinical settings. To foster further research, the code and datasets will be publicly released in the future.