A bias-resilient client selection analysis for federated brain tumor segmentation

Dynamic Curriculum Learning for Spatiotemporal Encoding (DCL-SE) is introduced as an innovative framework aimed at enhancing high-dimensional neuroimaging analyses for clinical diagnosis. By utilizing Approximate Rank Pooling (ARP), DCL-SE efficiently encodes three-dimensional brain data into two-dimensional representations. The framework employs a dynamic curriculum learning strategy to progressively refine feature extraction, demonstrating effectiveness across various datasets, including Alzheimer's disease and brain tumor classification.

Foundation models (FMs) are revolutionizing medical image analysis by leveraging large datasets of unlabeled data. Unlike traditional methods that depend on manually annotated examples, FMs are pre-trained to extract general visual features, which can be fine-tuned for specific clinical tasks with minimal supervision. This review explores the development and application of FMs in pathology, radiology, and ophthalmology, synthesizing insights from over 150 studies. It highlights the components of FM pipelines and discusses challenges and future research directions.

Selecting an appropriate look-back horizon is a key challenge in time series forecasting (TSF), especially in federated learning contexts where data is decentralized and heterogeneous. This paper proposes a framework for adaptive horizon selection in federated TSF using an intrinsic space formulation. It introduces a synthetic data generator that captures essential temporal structures in client data, such as autoregressive dependencies and seasonality, while considering client-specific variations.

Recent research highlights a new class of attacks in federated learning that compromise model interpretability without impacting accuracy. The study reveals that adversarial clients can apply small color perturbations, shifting a model's saliency maps from meaningful regions while maintaining predictions. This method, termed the Chromatic Perturbation Module, systematically creates adversarial examples by altering color contrasts, leading to persistent poisoning of the model's internal feature attributions, challenging assumptions about model reliability.

The X-VMamba model introduces a controllability-based interpretability framework for State Space Models (SSMs), particularly the Mamba architecture. This framework aims to clarify how Vision SSMs process spatial information, which has been a challenge due to the absence of transparent mechanisms. The proposed methods include a Jacobian-based approach for any SSM architecture and a Gramian-based method for diagonal SSMs, both designed to enhance understanding of internal state dynamics while maintaining computational efficiency.

Disney star has introduced AI avatars representing deceased individuals, marking a significant development in the intersection of entertainment and artificial intelligence. This debut showcases the potential of AI technology to create lifelike representations of those who have passed away, raising questions about ethics and the future of digital personas. The event took place on November 17, 2025, and is expected to attract attention from both fans and industry experts alike.

The article reviews the work titled 'Exploring metaphors of AI: visualisations, narratives and perception,' highlighting the contributions of IceMing & Digit and Stochastic Parrots. It discusses how visual and narrative metaphors influence the understanding of artificial intelligence (AI). The research emphasizes the importance of these metaphors in shaping perceptions and fostering better images of AI, which is crucial in a rapidly evolving technological landscape. The work is licensed under CC-BY 4.0.

As passenger volumes surge, managing airport technology has become increasingly complex. A new wave of AI models is emerging to assist in synchronizing various systems within the airport tech stack, aiming to enhance operational efficiency and improve the overall passenger experience.