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PriorsMatch:prior-level pseudo-labels for semi-supervised object detection on medical images 认领 引用
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作者 Jiaqiang CHEN Lei ZHANG +3 位作者 Wenjie LIU Xin WEI Jiaqi LI Xian JIANG 《Frontiers of Computer Science》 SCIE EI CAS CSCD 2026年第6期171-173,共3页
1 Introduction Object detection in medical imaging is vital for lesion identification but faces accuracy challenges due to scarce annotated data.Deep learning-based detectors need extensive labeled data,yet medical im... 1 Introduction Object detection in medical imaging is vital for lesion identification but faces accuracy challenges due to scarce annotated data.Deep learning-based detectors need extensive labeled data,yet medical image annotation is labor-intensive and time-consuming[1].Semi supervised object detection(SSOD)addresses this by leveraging unlabeled data to boost performance.Recent SSOD advancements integrate consistency regularization and pseudo-labeling within the MeanTeacher framework,where a teacher model generates pseudo labels from weakly augmented unlabeled data,and the student model is trained on strongly augmented versions.This enforces consistency in predictions,improving generalization.However,in medical imaging,where annotated data is scarce,these methods perform poorly compared to natural image applications. 展开更多
关键词 meanteacher frameworkwhere labeled datayet medical image leveraging unlabeled data object detection teacher model g consistency regularization supervised object detection ssod addresses
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Polariton lattices as binarized neuromorphic networks 认领 引用 被引量：2
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作者 Evgeny Sedov Alexey Kavokin 《Light: Science & Applications》 SCIE EI CAS CSCD 2025年第2期384-400,共17页
We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates,intricately interconnected and energized through nonresonant optical pumping.The network employs a binary fram... We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates,intricately interconnected and energized through nonresonant optical pumping.The network employs a binary framework,where each neuron,facilitated by the spatial coherence of pairwise coupled condensates,performs binary operations.This coherence,emerging from the ballistic propagation of polaritons,ensures efficient,network-wide communication.The binary neuron switching mechanism,driven by the nonlinear repulsion through the excitonic component of polaritons,offers computational efficiency and scalability advantages over continuous weight neural networks.Our network enables parallel processing,enhancing computational speed compared to sequential or pulse-coded binary systems.The system's performance was evaluated using diverse datasets,including the MNiST dataset for image recognition and the Speech Commands dataset for voice recognition tasks.In both scenarios,the proposed system demonstrates the potential to outperform existing polaritonic neuromorphic systems.For image recognition,this is evidenced by an impressive predicted classification accuracy of up to 97.5%.In voice recognition,the system achieved a classification accuracy of about 68%for the ten-class subset,surpassing the performance of conventional benchmark,the Hidden Markov Model with Gaussian Mixture Model. 展开更多
关键词 spatial coherence nonlinear r binary frameworkwhere binary neuron switching mechanismdriven neuromorphic network architecture polariton lattices nonresonant optical pumpingthe neuromorphic networks
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