ReliableRAG: Combating Misinformation in Retrieval-Augmented Generation via Reliability-Guided Reasoning Chains
针对检索增强生成中虚假信息问题，提出ReliableRAG框架，通过细粒度评估和构建可靠的推理链来提高答案的准确性和鲁棒性。
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Current AI research on color fundus photography (CFP) lacks a systematic perspective on the co-evolution of datasets, preprocessing, and modeling. This work proposes the first unified framework that synergistically optimizes data curation, preprocessing, and multimodal modeling, integrating neural data engineering, hardware-aware annotation, self-supervised electronic health record imputation, vision foundation models, state space models, and multimodal mixture-of-experts architectures. The study delineates a clear evolutionary trajectory for CFP analysis—from single-task convolutional neural networks toward multimodal, longitudinally integrated clinical systems—and provides a methodological roadmap toward robust ophthalmic AI capable of clinical deployment, cross-domain generalization, and edge intelligence.
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This study addresses the challenge of joint segmentation and classification in fetal echocardiography under severe label scarcity by proposing a semi-supervised multi-task learning framework. Built upon the EchoCare backbone, the method integrates SAM-Med2D for boundary refinement and leverages DINOv3 to enhance pseudo-label quality. A novel view-specific hard masking mechanism and a two-stage optimization strategy are introduced: the first stage employs exponential moving average (EMA) to boost segmentation performance, while the second stage freezes segmentation parameters and resets the classification head to restore discriminative capability. Evaluated on the FETUS 2026 benchmark, the model achieves a Dice coefficient of 79.99%, a normalized surface distance of 61.62%, and an F1 score of 41.20%, significantly outperforming existing approaches.
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ReliableRAG: Combating Misinformation in Retrieval-Augmented Generation via Reliability-Guided Reasoning Chains
针对检索增强生成中虚假信息问题，提出ReliableRAG框架，通过细粒度评估和构建可靠的推理链来提高答案的准确性和鲁棒性。
0 citationsRead paper
Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling
This study addresses the ambiguity surrounding the methodological evolution of protein structure prediction by proposing a "four-stage, three-transition" analytical framework. By systematically examining technical paradigm shifts across representation, architecture, and evaluation dimensions, this research integrates deep learning and generative modeling to elucidate the intrinsic trajectory from evolutionary constraints to generative design. Consequently, this work constructs a comprehensive methodological evolution map that clarifies historical transitions in model capabilities and application roles. Ultimately, it provides a systematic theoretical foundation for understanding the developmental logic and future trends within the field, offering critical insights into how predictive methodologies have matured over time.
0 citationsRead paper
Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI
Current AI research on color fundus photography (CFP) lacks a systematic perspective on the co-evolution of datasets, preprocessing, and modeling. This work proposes the first unified framework that synergistically optimizes data curation, preprocessing, and multimodal modeling, integrating neural data engineering, hardware-aware annotation, self-supervised electronic health record imputation, vision foundation models, state space models, and multimodal mixture-of-experts architectures. The study delineates a clear evolutionary trajectory for CFP analysis—from single-task convolutional neural networks toward multimodal, longitudinally integrated clinical systems—and provides a methodological roadmap toward robust ophthalmic AI capable of clinical deployment, cross-domain generalization, and edge intelligence.
0 citationsRead paper
Synergistic Foundation Models for Semi-Supervised Fetal Cardiac Ultrasound Analysis: SAM-Med2D Boundary Refinement and DINOv3 Semantic Enhancement
This study addresses the challenge of joint segmentation and classification in fetal echocardiography under severe label scarcity by proposing a semi-supervised multi-task learning framework. Built upon the EchoCare backbone, the method integrates SAM-Med2D for boundary refinement and leverages DINOv3 to enhance pseudo-label quality. A novel view-specific hard masking mechanism and a two-stage optimization strategy are introduced: the first stage employs exponential moving average (EMA) to boost segmentation performance, while the second stage freezes segmentation parameters and resets the classification head to restore discriminative capability. Evaluated on the FETUS 2026 benchmark, the model achieves a Dice coefficient of 79.99%, a normalized surface distance of 61.62%, and an F1 score of 41.20%, significantly outperforming existing approaches.
0 citationsRead paper