| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Adam White, Margarita Saranti, Artur d'Avila Garcez, Thomas M H Hope, Cathy J Price, Howard Bowma. Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI. NeuroImage. Clinical. vol 43. 2024-07-13. PMID:39002223. |
we evaluate a series of cnn architectures (both 2d and a 3d) that are trained on different representations of mri and tabular data, to predict whether a composite measure of post-stroke spoken picture description ability is in the aphasic or non-aphasic range. |
2024-07-13 |
2024-07-16 |
human |
| Xin Bi, Tian Zhan. Pedagogical sentiment analysis based on the BERT-CNN-BiGRU-attention model in the context of intercultural communication barriers. PeerJ. Computer science. vol 10. 2024-07-10. PMID:38983236. |
it proposes a hybrid model (bcba) based on bidirectional encoder representations from transformers (bert), convolutional neural networks (cnn), bidirectional gated recurrent units (bigru), and the attention mechanism. |
2024-07-10 |
2024-07-12 |
Not clear |
| Alex Mirugwe, Clare Ashaba, Alice Namale, Evelyn Akello, Edward Bichetero, Edgar Kansiime, Juwa Nyirend. Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers. Life (Basel, Switzerland). vol 14. issue 6. 2024-06-27. PMID:38929691. |
the techniques examined included a 6-layer convolutional neural network (cnn), a 6-layer long short-term memory model (lstm), and an 8-layer bidirectional encoder representations from transformers (bert) model. |
2024-06-27 |
2024-06-29 |
Not clear |
| Rongtao Xu, Changwei Wang, Jiguang Zhang, Shibiao Xu, Weiliang Meng, Xiaopeng Zhan. SkinFormer: Learning Statistical Texture Representation With Transformer for Skin Lesion Segmentation. IEEE journal of biomedical and health informatics. vol PP. 2024-06-24. PMID:38913520. |
texture representations are not only related to the local structural information learned by cnn, but also include the global statistical texture information of the input image. |
2024-06-24 |
2024-06-27 |
Not clear |
| Karim Gasmi, Hajer Ayadi, Mouna Torjme. Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing. Diagnostics (Basel, Switzerland). vol 14. issue 11. 2024-06-19. PMID:38893730. |
specifically, our dmm aims to generate effective representations for query and image metadata using a personalized cnn, facilitating matching between these representations. |
2024-06-19 |
2024-06-21 |
Not clear |
| Jianqiao Sun, Bo Chen, Ruiying Lu, Ziheng Cheng, Chunhui Qu, Xin Yua. Advancing Hyperspectral and Multispectral Image Fusion: An Information-Aware Transformer-Based Unfolding Network. IEEE transactions on neural networks and learning systems. vol PP. 2024-05-23. PMID:38776209. |
with the powerful representation ability, convolutional neural network (cnn)-based deep unfolding methods have demonstrated promising performances. |
2024-05-23 |
2024-05-27 |
Not clear |
| Chuanxia Zheng, Guoxian Song, Tat-Jen Cham, Jianfei Cai, Linjie Luo, Dinh Phun. Bridging Global Context Interactions for High-Fidelity Pluralistic Image Completion. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2024-05-21. PMID:38771691. |
our key contribution is to introduce a code-shared codebook learning using a restrictive cnn on small and non-overlapping receptive fields (rfs) for the local visible token representation. |
2024-05-21 |
2024-05-27 |
Not clear |
| Weiwei Cao, Jianfeng Guo, Xiaohui You, Yuxin Liu, Lei Li, Wenju Cui, Yuzhu Cao, Xinjian Chen, Jian Zhen. NeighborNet: Learning Intra- and Inter-Image Pixel Neighbor Representation for Breast Lesion Segmentation. IEEE journal of biomedical and health informatics. vol PP. 2024-05-15. PMID:38743530. |
with the two properties, for each pixel at each feature level, the proposed neighbornet can evolve into the transformer or degenerate into the cnn for adaptive context representation learning to cope with the irregular lesion morphologies and blurry boundaries. |
2024-05-15 |
2024-05-27 |
human |
| Shuang Zhou, Meiling Du, XiaoYu Liu, Hongyan She. Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network. Heliyon. vol 10. issue 9. 2024-05-10. PMID:38720748. |
these traditional models include convolutional neural network (cnn), long short-term memory network (lstm), bidirectional encoder representations from transformers (bert), generative pre-trained transformer (gpt), and extreme gradient boosting (xgboost). |
2024-05-10 |
2024-05-27 |
Not clear |
| Chengxuan Tong, Yi Ding, Zhuo Zhang, Haihong Zhang, Kevin JunLiang Lim, Cuntai Gua. TASA: Temporal Attention with Spatial Autoencoder Network for Odor-induced Emotion Classification Using EEG. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol PP. 2024-05-10. PMID:38722724. |
we improved upon the previous work by utilizing a two-phase learning framework, using the autoencoder module to learn the spatial information among electrodes by reconstructing the given input with a latent representation in the spatial dimension, which aims to minimize information loss compared to spatial filtering with cnn. |
2024-05-10 |
2024-05-27 |
human |
| Delei Wang, Yanqing Ya. Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears. Scientific reports. vol 14. issue 1. 2024-04-29. PMID:38684782. |
these findings surpass current methodologies like convolutional neural network (cnn), inception-v3, deep belief networks and improved honey badger algorithm (dbn/ihba), integration of the cnn with an amended cooking training-based optimizer version (cnn/acto), self-supervised representation learning (ssrl), signifying a significant breakthrough in acl injury diagnosis. |
2024-04-29 |
2024-05-03 |
Not clear |
| Jun Hu, Zhe Li, Bing Rao, Maha A Thafar, Muhammad Ari. Improving Protein-Protein Interaction Prediction Using Protein Language Model and Protein Network Features. Analytical biochemistry. 2024-04-28. PMID:38679191. |
subsequently, feature representations are further extracted through cksaap, and a two-dimensional convolutional neural network (cnn) is utilized to capture local information. |
2024-04-28 |
2024-05-01 |
Not clear |
| Austin Spadaro, Alok Sharma, Iman Dehzang. Predicting lysine methylation sites using a convolutional neural network. Methods (San Diego, Calif.). 2024-04-11. PMID:38604414. |
automated feature extraction from these representations of amino acids as well as cnn as a classifier have never been used for this problem. |
2024-04-11 |
2024-04-14 |
Not clear |
| Siuly Siuly, Smith K Khare, Enamul Kabir, Muhammad Tariq Sadiq, Hua Wan. An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network. Computers in biology and medicine. vol 174. 2024-04-10. PMID:38599069. |
to address these limitations, this study proposes a novel approach using a time-frequency representation (tfr) based alexnet convolutional neural network (cnn) model to explore eeg channel-based analysis and identify critical brain regions efficiently diagnosing pd from eeg data. |
2024-04-10 |
2024-04-13 |
Not clear |
| Min Gao, Shaohua Jiang, Weibin Ding, Ting Xu, Zhijian Ly. Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling. Journal of bioinformatics and computational biology. vol 22. issue 1. 2024-04-03. PMID:38567388. |
furthermore, the incorporation of queries, keys and values produced by the stacked convolutional neural network (cnn) enables our model to better integrate the local and global context of protein representation, leading to further improvements in the accuracy of dta prediction. |
2024-04-03 |
2024-04-05 |
Not clear |
| Keling Fei, Jianghui Wang, Lizhen Pan, Xu Wang, Baohong Che. A sleep staging model on wavelet-based adaptive spectrogram reconstruction and light weight CNN. Computers in biology and medicine. vol 173. 2024-03-28. PMID:38547654. |
these spectrograms enabled a light weight cnn to detect and extract finer details of different sleep stages, which improved the feature representation of eeg. |
2024-03-28 |
2024-03-31 |
Not clear |
| Hutuo Quan, Huicheng Lai, Guxue Gao, Jun Ma, Junkai Li, Dongji Che. Pairwise CNN-Transformer Features for Human-Object Interaction Detection. Entropy (Basel, Switzerland). vol 26. issue 3. 2024-03-28. PMID:38539717. |
the enhanced representations are superior to using cnn and transformer features individually. |
2024-03-28 |
2024-03-30 |
Not clear |
| Hadi Sedigh Malekroodi, Nuwan Madusanka, Byeong-Il Lee, Myunggi Y. Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns. Bioengineering (Basel, Switzerland). vol 11. issue 3. 2024-03-27. PMID:38534569. |
popular convolutional neural network (cnn) architectures, vgg and resnet, as well as vision transformers, swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. |
2024-03-27 |
2024-03-29 |
human |
| Hassaan Malik, Tayyaba Anee. Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds. PloS one. vol 19. issue 3. 2024-03-12. PMID:38470893. |
thus, we suggested four novel convolutional neural network (cnn) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. |
2024-03-12 |
2024-03-15 |
Not clear |
| Xinning Jin, Zhiqiang Wang, Jingyu Ma, Chuanzheng Liu, Xuerui Bai, Yubin La. Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of astragalus. Journal of the science of food and agriculture. 2024-03-09. PMID:38459895. |
the proposed model employs an improved transformer module and an improved ghost bottleneck as its backbone network, complementarily utilizing the benefits of cnn and transformer architectures for local and global feature representation. |
2024-03-09 |
2024-03-12 |
Not clear |