Deep learningSpectroscopyConvolutional neural networkAnalysisThe development of chemometrics aims to provide an effective analysis approach for data generated by advanced analytical instruments. The success of existing analytical approaches in spectral analysis still relies on preprocessing and feature selection ...
spectral reconstruction. To boost future researches on learned spectral imaging, we have organized existing spectral datasets and the evaluation metrics (in “Spectral Imaging Datasets”). Finally, we will summarize the deep-learning-empowered spectral imaging methods in “Conclusions and Future Directions...
Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large...
Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large...
Novel optical imaging techniques, such as hyperspectral imaging (HSI) combined with machine learning-based (ML) analysis, have the potential to revolutionize clinical surgical imaging. However, these novel modalities face a shortage of large-scale, representative clinical data for training ML algorithms...
Deep learning is a complex machine learning algorithm that involves learning inherent rules and representation levels of sample data through large neural networks with multiple layers. It is popular for its automatic feature extraction capabilities and is applied in various areas such as CNN, LSTM, RN...
The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a pro
Using deep learning to generate in silico spectral libraries for data-independent acquisition analysis. - GitHub - lmsac/DeepDIA: Using deep learning to generate in silico spectral libraries for data-independent acquisition analysis.
Validation sets are used alongside training sets to verify the performance of the model, which can prevent overfitting in model training and allow us to fine-tune hyperparameters of different learning layers23. The analysis workflow of AirSurf-Lettuce The analysis of yield-related phenotypes was ...
Monitoring disease progression necessitates the accurate segmentation and quantification of these cavities; yet, current manual methods are time consuming and result in subjective interpretations, highlighting the need for automated and precise solutions. We employed five state-of-the-art deep learning ...