Medical application of deep-learning-based head pose estimation from RGB image sequence [0.03%]
基于深度学习的RGB图像序列头部姿态估计的医学应用
Kittisak Chotikkakamthorn,Wen-Nung Lie,Panrasee Ritthipravat et al.
Kittisak Chotikkakamthorn et al.
This study aims to propose the application of a deep neural network adopting multi-level pyramidal feature extraction, a bi-directional Pyramidal Feature Aggregation Structure (PFAS) for feature fusion, a modified Atrous Spatial Pyramid Pooling (ASPP) module for spatial and channel feature enhancement
Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment [0.03%]
半干旱环境下用于模拟每日泛蒸发的独立和混合深度神经网络的评估比较研究
Mohammed Achite,Manish Kumar,Nehal Elshaboury et al.
Mohammed Achite et al.
Conventional deep neural network (DNN) coupled with support vector machine (SVM), Bayesian additive regression trees (BART), random subspace (RSS), M5 pruned, and random forest (RF) were used for developing prediction models using various input variable combinations.
Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections [0.03%]
深度学习检测急性颈部感染患者MRI中的咽后间隙水肿
Oona Rainio,Heidi Huhtanen,Jari-Pekka Vierula et al.
Oona Rainio et al.
Methods: We developed a deep neural network consisting of two parts using axial T2-weighted water-only Dixon MRI images from 479 patients with acute neck infections annotated by radiologists at both slice and patient levels.
Smartphone eye-tracking with deep learning: Data quality and field testing [0.03%]
基于深度学习的智能手机眼球追踪：数据质量和实地测试
Gancheng Zhu,Zehao Huang,Xiaoting Duan et al.
Gancheng Zhu et al.
This paper presents a real-time smartphone eye-tracking system built upon a deep neural network trained on a dataset of 7.4 million facial images. The tracking performance of the system was benchmarked against an industrial gold-standard EyeLink eye tracker using a reasonably large sample (N = 32).
Neuromimetic metaplasticity for adaptive continual learning without catastrophic forgetting [0.03%]
基于神经形态元塑性的自适应连续学习避免灾难性遗忘
Suhee Cho,Hyeonsu Lee,Seungdae Baek et al.
Suhee Cho et al.
Conventional intelligent systems based on deep neural network (DNN) models encounter challenges in achieving human-like continual learning due to catastrophic forgetting.
Explainable AI predicting Alzheimer's disease with latent multimodal deep neural networks [0.03%]
基于潜在多模态深度神经网络的可解释AI预测阿尔茨海默病
Xi Chen,Jeffrey Thompson,Zijun Yao et al.
Xi Chen et al.
A multimodal deep neural network with three modalities, including clinical data, imaging data, and genetic data, was constructed. Attention layers and cross attention layers were added to improve prediction; modality importance scores were calculated for interpretation.
Sensitivity-Enhanced Pure Shift Spectroscopy Empowered by Deep Learning and PSYCHE [0.03%]
基于深度学习和PSYCHE的灵敏度增强型纯位移光谱学
Xiaoxu Zheng,Wen Zhu,Xiaoqi Shi et al.
Xiaoxu Zheng et al.
Then, a deep neural network model is employed to remove recoupling artifacts to obtain a clean spectrum. The model can correctly recognize all peaks, remove recoupling artifacts and chunking sidebands, and retain the desired pure shift peaks.
Abhinav S Raman,Annabella Selloni
Abhinav S Raman
Herein, we focus on two common atmospheric carboxylic acids, formic and acetic acid, and characterize their adsorption/ desorption at the aqueous interfaces of anatase and rutile TiO2 using molecular dynamics with an ab initio deep neural network potential.
Katrina P Nguyen,Abigail L Person
Katrina P Nguyen
The rise of the deep neural network as the workhorse of artificial intelligence has brought increased attention to how network architectures serve specialized functions. The cerebellum, with its largely shallow, feedforward architecture, provides a curious example of such a specialized network.
Decoding Parkinson's Diagnosis: An OCT-Based Explainable AI with SHAP/LIME Transparency from the Persian Cohort Study [0.03%]
基于OCT的可解释人工智能在帕金森诊断中的应用：来自波斯队列研究的SHAP/LIME透明度分析
Zohreh Ganji,Farzane Nikparast,Naser Shoeibi et al.
Zohreh Ganji et al.
Methods: Leveraging data from the Persian Cohort Study (202 PD patients, 972 controls), we developed a 6-layer deep neural network (DNN) combining OCT biomarkers (foveal thickness and volume) and clinical variables (motor scores, olfactory dysfunction).
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