| Automated depression analysis using convolutional neural networks from speech L He, C Cao Journal of biomedical informatics 83, 103-111, 2018 | 204 | 2018 |
| Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks L He, D Jiang, L Yang, E Pei, P Wu, H Sahli ACM MM2015, pp.73-80, 2015 | 192 | 2015 |
| Decision Tree Based Depression Classification from Audio Video and Language Information L Yang, D Jiang, L He, E Pei, MC Oveneke, H Sahli ACM MM2016, pp. 89-96, 2016 | 145 | 2016 |
| Deep learning for depression recognition with audiovisual cues: A review L He, M Niu, P Tiwari, P Marttinen, R Su, J Jiang, C Guo, H Wang, S Ding, ... Information Fusion 80, 56-86, 2022 | 104 | 2022 |
| Automatic depression recognition using CNN with attention mechanism from videos L He, JCW Chan, Z Wang Neurocomputing 422, 165-175, 2021 | 90 | 2021 |
| Automatic depression analysis using dynamic facial appearance descriptor and dirichlet process fisher encoding L He, D Jiang, H Sahli IEEE Transactions on Multimedia 21 (6), 1476-1486, 2018 | 71 | 2018 |
| Multimodal depression recognition with dynamic visual and audio cues L He, D Jiang, H Sahli 2015 International Conference on Affective Computing and Intelligent …, 2015 | 53 | 2015 |
| Intelligent system for depression scale estimation with facial expressions and case study in industrial intelligence L He, C Guo, P Tiwari, HM Pandey, W Dang International Journal of Intelligent Systems 37 (12), 10140-10156, 2022 | 32 | 2022 |
| DepNet: An automated industrial intelligent system using deep learning for video‐based depression analysis L He, C Guo, P Tiwari, R Su, HM Pandey, W Dang International Journal of Intelligent Systems 37 (7), 3815-3835, 2022 | 16 | 2022 |
| Reducing noisy annotations for depression estimation from facial images L He, P Tiwari, C Lv, WS Wu, L Guo Neural Networks 153, 120-129, 2022 | 12 | 2022 |
| 基于事件驱动的面向服务计算模型 何浪， 史维峰， 董建刚 计算机工程 36 (18), 57-59, 2010 | 10 | 2010 |
| Depressioner: facial dynamic representation for automatic depression level prediction M Niu, L He, Y Li, B Liu Expert Systems with Applications 204, 117512, 2022 | 9 | 2022 |
| Audio–visual collaborative representation learning for dynamic saliency prediction H Ning, B Zhao, Z Hu, L He, E Pei Knowledge-Based Systems 256, 109675, 2022 | 7 | 2022 |
| CovidNet: An automatic architecture for Covid-19 detection with deep learning from chest X-ray images L He, P Tiwari, R Su, X Shi, P Marttinen, N Kumar IEEE Internet of Things Journal 9 (13), 11376-11384, 2021 | 5 | 2021 |
| A novel Image-Data-Driven and Frequency-Based method for depression detection J Zhao, L Zhang, Y Cui, J Shi, L He Biomedical Signal Processing and Control 86, 105248, 2023 | 3 | 2023 |
| CPSS-FAT: A consistent positive sample selection for object detection with full adaptive threshold X Yang, J Wu, L He, S Ma, Z Hou, W Sun Pattern Recognition 141, 109627, 2023 | 3 | 2023 |
| An improved global-local fusion network for depression detection telemedicine framework L Zhang, J Zhao, L He, J Jia, X Meng IEEE Internet of Things Journal, 2023 | 3 | 2023 |
| Relate auditory speech to EEG by shallow-deep attention-based network F Cui, L Guo, L He, J Liu, EC Pei, Y Wang, D Jiang ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 2 | 2023 |
| 基于3DCNN和时空注意力卷积LSTM的抑郁症识别研究 何浪 首都师范大学学报 42 (2), 17-25, 2021 | 2 | 2021 |
| Bio-acoustic emotion recognition using continuous conditional recurrent neural fields N Banda, L He, A Engelbrecht 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2017 | 2 | 2017 |
