spectrai: A deep learning framework for spectral data
This framework addresses the problem of applying deep learning to spectral data for researchers and practitioners, but it is incremental as it builds on existing deep learning techniques.
The authors tackled the complexity of applying deep learning to spectral data by developing spectrai, an open-source framework that facilitates training and comparison of neural networks for tasks like denoising, classification, segmentation, and super-resolution, with built-in pre-processing and augmentation methods.
Deep learning computer vision techniques have achieved many successes in recent years across numerous imaging domains. However, the application of deep learning to spectral data remains a complex task due to the need for augmentation routines, specific architectures for spectral data, and significant memory requirements. Here we present spectrai, an open-source deep learning framework designed to facilitate the training of neural networks on spectral data and enable comparison between different methods. Spectrai provides numerous built-in spectral data pre-processing and augmentation methods, neural networks for spectral data including spectral (image) denoising, spectral (image) classification, spectral image segmentation, and spectral image super-resolution. Spectrai includes both command line and graphical user interfaces (GUI) designed to guide users through model and hyperparameter decisions for a wide range of applications.