| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Tianyuan Liu, Junyang Huang, Delun Luo, Liping Ren, Lin Ning, Jian Huang, Hao Lin, Yang Zhan. Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy. International journal of biological macromolecules. 2024-03-09. PMID:38460652. |
it incorporates a cross-attention model to globally correlate different representation vectors and a two-layer cnn module to learn local correlation features. |
2024-03-09 |
2024-03-12 |
Not clear |
| Md Sultan Mahmud, Oishy Saha, Shaikh Anowarul Fattah, Mohammad Saqui. Emotion Recognition with Reduced Channels Using CWT Based EEG Feature Representation and a CNN Classifier. Biomedical physics & engineering express. 2024-03-08. PMID:38457844. |
emotion recognition with reduced channels using cwt based eeg feature representation and a cnn classifier. |
2024-03-08 |
2024-03-11 |
Not clear |
| Dongmin Huang, Dongfang Yu, Yongshen Zeng, Xiaoyan Song, Liping Pan, Junli He, Lirong Ren, Jie Yang, Hongzhou Lu, Wenjin Wan. Generalized Camera-Based Infant Sleep-Wake Monitoring in NICUs: A Multi-Center Clinical Trial. IEEE journal of biomedical and health informatics. vol PP. 2024-03-07. PMID:38446652. |
using the face videos of 64 term and 39 preterm infants recorded in nicus, we proposed a novel sleep-wake classification strategy, called consistent deep representation constraint (cdrc), that forces the convolutional neural network (cnn) to make consistent predictions for the samples from different conditions but with the same label, to address the variances caused by infants and environments. |
2024-03-07 |
2024-03-09 |
Not clear |
| Jianning Chi, Jin Zhao, Siqi Wang, Xiaosheng Yu, Chengdong W. LGDNet: local feature coupling global representations network for pulmonary nodules detection. Medical & biological engineering & computing. 2024-03-01. PMID:38429443. |
to overcome the limited long-range dependency capabilities inherent in convolutional operations, a dual-branch module is designed to integrate the convolutional neural network (cnn) branch that extracts local features with the transformer branch that captures global representations. |
2024-03-01 |
2024-03-04 |
Not clear |
| Meghana V Palukuri, Edward M Marcott. DeepSLICEM: Clustering CryoEM particles using deep image and similarity graph representations. bioRxiv : the preprint server for biology. 2024-02-19. PMID:38370702. |
deepslicem explores 6 pretrained convolutional neural networks and one supervised siamese cnn for image representation, 10 pretrained deep graph neural networks for similarity graph node representations, and 4 methods for clustering, along with 8 methods for directly clustering the similarity graph. |
2024-02-19 |
2024-02-21 |
Not clear |
| Linshu Wang, Yuan Zho. MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features. RNA biology. vol 21. issue 1. 2024-02-15. PMID:38357904. |
we developed mrm-bert, a deep learning method that combined the pre-trained dnabert deep sequence representation module and the convolutional neural network (cnn) exploiting four traditional sequence feature encodings to improve the prediction performance. |
2024-02-15 |
2024-02-17 |
Not clear |
| Abinaya K, Sivakumar . A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer. Journal of imaging informatics in medicine. 2024-02-12. PMID:38343216. |
the proposed model leverages the capability of 3d cnn to extract spatiotemporal features from cervical images and employs the vit model to capture and learn complex feature representations. |
2024-02-12 |
2024-02-15 |
Not clear |
| Zihan Li, Yuan Zheng, Dandan Shan, Shuzhou Yang, Qingde Li, Beizhan Wang, Yuanting Zhang, Qingqi Hong, Dinggang She. ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation. IEEE transactions on medical imaging. vol PP. 2024-02-07. PMID:38324425. |
specifically, the cnn branch collaborates with the transformer branch to fuse the local features learned from cnn with the global representations obtained from transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. |
2024-02-07 |
2024-02-10 |
Not clear |
| Yuyang Sha, Weiyu Meng, Gang Luo, Xiaobing Zhai, Henry H Y Tong, Yuefei Wang, Kefeng L. MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks. Analytical chemistry. 2024-02-07. PMID:38324756. |
netomics, the second component, leverages a cnn architecture to extract more discriminative representations from the transformed samples. |
2024-02-07 |
2024-02-10 |
Not clear |
| Jiacheng Tang, Qi Kang, MengChu Zhou, Hao Yin, Siya Ya. MemeNet: Towards a Reliable Local Projection for Image Recognition via Semantic Featurization. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2024-02-02. PMID:38306266. |
first, local representations named memes are extracted from the activation map of a cnn model. |
2024-02-02 |
2024-02-05 |
Not clear |
| Xingyue Gu, Junkai Liu, Yue Yu, Pengfeng Xiao, Yijie Din. MFD-GDrug: Multimodal feature fusion-based deep learning for GPCR-drug interaction prediction. Methods (San Diego, Calif.). 2024-01-29. PMID:38286333. |
leveraging the esm pretrained model, we extract protein features and employ a cnn for protein feature representation. |
2024-01-29 |
2024-02-01 |
Not clear |
| Ihssan S Masad, Amin Alqudah, Shoroq Qaza. Automatic classification of sleep stages using EEG signals and convolutional neural networks. PloS one. vol 19. issue 1. 2024-01-26. PMID:38277364. |
the proposed methodology consists of three major steps: (i) segment the eeg signal into epochs with 30 seconds in length, (ii) convert epochs into 2d representation using time-frequency analysis, and (iii) feed the 2d time-frequency analysis to the 2d cnn. |
2024-01-26 |
2024-01-29 |
human |
| Klaifer Garcia, Pedro Shiguihara, Lilian Berto. Breaking news: Unveiling a new dataset for Portuguese news classification and comparative analysis of approaches. PloS one. vol 19. issue 1. 2024-01-26. PMID:38277376. |
second, we compare different architectures for portuguese news classification, exploring different text representations (bow, tf-idf, embedding) and classification techniques (svm, cnn, djinn, bert) for documents in portuguese, covering classical methods and current technologies. |
2024-01-26 |
2024-01-29 |
human |
| Monika Khandelwal, Ranjeet Kumar Rou. DeepPRMS: advanced deep learning model to predict protein arginine methylation sites. Briefings in functional genomics. 2024-01-24. PMID:38267081. |
we combined the latent representation of gru and cnn models to have a better interaction among them. |
2024-01-24 |
2024-01-27 |
Not clear |
| Yinghua Fu, Junfeng Liu, Jun Sh. TSCA-Net: Transformer based spatial-channel attention segmentation network for medical images. Computers in biology and medicine. vol 170. 2024-01-14. PMID:38219644. |
the proposed network inherits the advantages of both cnn and transformer with the local feature representation and long-range dependency for medical images. |
2024-01-14 |
2024-01-17 |
Not clear |
| Sara Akan, Songül Varlı, Mohammad Alfrad Nobel Bhuiya. An enhanced Swin Transformer for soccer player reidentification. Scientific reports. vol 14. issue 1. 2024-01-11. PMID:38212392. |
despite the great success of current convolutional neural network-based (cnn) methods, most studies only consider learning representations from images, neglecting long-range dependency. |
2024-01-11 |
2024-01-14 |
Not clear |
| Igor Zingman, Birgit Stierstorfer, Charlotte Lempp, Fabian Heineman. Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development. Medical image analysis. vol 92. 2023-12-23. PMID:38141454. |
such approaches combined with pre-trained convolutional neural network (cnn) representations of images were previously employed for anomaly detection (ad). |
2023-12-23 |
2023-12-25 |
Not clear |
| Igor Zingman, Birgit Stierstorfer, Charlotte Lempp, Fabian Heineman. Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development. Medical image analysis. vol 92. 2023-12-23. PMID:38141454. |
however, pre-trained off-the-shelf cnn representations may not be sensitive to abnormal conditions in tissues, while natural variations of healthy tissue may result in distant representations. |
2023-12-23 |
2023-12-25 |
Not clear |
| Igor Zingman, Birgit Stierstorfer, Charlotte Lempp, Fabian Heineman. Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development. Medical image analysis. vol 92. 2023-12-23. PMID:38141454. |
to adapt representations to relevant details in healthy tissue we propose training a cnn on an auxiliary task that discriminates healthy tissue of different species, organs, and staining reagents. |
2023-12-23 |
2023-12-25 |
Not clear |
| X Wang, S Leng, Z Lu, S Huang, B H Lee, L Baskaran, M S Yew, L Teo, M Y Chan, K Y Ngiam, H K Lee, L Zhong, W Huan. Context-aware deep network for coronary artery stenosis classification in coronary CT angiography. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2023. 2023-12-12. PMID:38083399. |
the proposed method integrates 3d cnn with transformer to improve the feature representation of coronary artery stenosis in ccta. |
2023-12-12 |
2023-12-17 |
Not clear |