Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, radiotherapy targeting, and longitudinal assessment. Deep learning has advanced this task through convolutional neural networks, Transformers, state-space models, diffusion methods, and foundation models. However, strong benchmark performance does not guarantee clinical reliability because systems remain vulnerable to missing or degraded modalities, cross-center shift, and subregion-specific failures, particularly in enhancing tumor (ET) and tumor core (TC). This survey critically reviews deep learning for multimodal brain tumor segmentation from a deployment-oriented, failure-focused perspective. We introduce a unifying framework linking input reliability, fusion-architecture co-design, subregion-specific failure mechanisms, and uncertainty-aware clinical triage. Using this framework, we compare major architectural paradigms in contextual modeling, boundary preservation, computational feasibility, and robustness across ET, TC, and whole tumor (WT). We reinterpret multimodal fusion as a reliability-allocation problem and examine early, intermediate, token-level, sequence-aware, and adaptive strategies under incomplete or degraded inputs. We also synthesize robust learning approaches for sparse supervision, MRI quality degradation, cross-center variation, and test-time adaptation, and assess interpretability, uncertainty estimation, and human-in-the-loop review as mechanisms for clinical risk control. Finally, our evidence-oriented benchmarking analysis identifies WT performance saturation, persistent ET/TC instability, inconsistent boundary-metric reporting, and insufficient stress testing, center-stratified evaluation, calibration assessment, and computational transparency. We conclude that progress should be judged not only by benchmark accuracy but also by subregion-level reliability under realistic deployment conditions.
Yi Zhou, J. Kanesan, C. Chow et al.· Computerized Medical Imaging...· 0 citations
Quadruped robots have attracted increasing attention because they can traverse uneven terrain, support field deployment, and perform tasks that are difficult for wheeled or tracked platforms. Recent advances in artificial intelligence (AI) have further expanded their capabilities from manually designed gait control toward learning-based locomotion, perception-aware adaptation, dynamic motion skills, autonomous recovery, manipulation, energy-aware operation, fault diagnosis, and human–robot interaction. However, the literature on AI-driven quadruped robotics is distributed across diverse technical topics, robot platforms, validation settings, and performance metrics, making it difficult to assess the maturity and practical value of different approaches. To address this need, this review provides an AI-centered and deployment-oriented overview of quadruped robotics. A systematic literature search was conducted using Web of Science, IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink, covering studies published approximately from 2000 to 2025. After screening and eligibility assessment, 287 studies were included for detailed review. The review first examines AI-driven locomotion, including reinforcement learning, non-RL machine-learning methods, model-based approaches, and hybrid strategies, with attention to robustness, sim-to-real transfer, sensor use, computational requirements, and hardware validation. It then summarizes AI-supported advanced behaviors, including jumping, fall prevention and recovery, and object manipulation, focusing on reported quantitative performance, impact management, and reliability. Finally, it discusses system-level topics that affect real-world deployment, including fault diagnosis, energy-efficient control, shared autonomy, trust-aware and explainable interaction, and safety-aware human–robot collaboration. By organizing the literature according to robot capabilities, validation maturity, and deployment challenges, this review helps clarify the current progress, limitations, and future directions of AI-driven quadruped robots.
Li-Kai Wu, C. Chow, W. Wong et al.· Frontiers in Neurorobotics· 0 citations
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