Scientists have developed a device that works with a smartphone or tablet to capture medical images which can identify infected wounds. By capturing the heat produced by a wound and the fluorescence of bacteria, it helps clinicians tell the difference between inflammation and a potentially dangerous infection. This could allow for quicker intervention, catching infections before they become serious threats to health.
Hyperspectral image segmentation is an emerging area with numerous applications, including agriculture, forestry, environment monitoring, and remote sensing. This paper proposes a new neural architecture search algorithm, named AdaptorNAS, for hyperspectral image segmentation. AdaptorNAS aims to design the optimum decoder for any given encoder. In our approach, the search space of AdaptorNAS is a large deep neural network (DNN), and the optimal decoder is derived by pruning the large DNN via a perturbation-based pruning strategy. Verified on three popular encoders, i.e., ResNet-34, MobileNet-V2, and EfficientNet-B2, AdaptorNAS can design high-speed decoders that are significantly better than six common hand-crafted decoders. Additionally, with the EfficientNet-B2 encoder, AdaptorNAS (mIoU of 92.47% and mDice of 95.15%) outperforms the state-of-the-art NAS algorithms and hand-crafted network architectures on the hyperspectral image segmentation task. We also introduce a new hyperspectra
/PRNewswire/ The "Optical Imaging Techniques: Global Strategic Business Report" report has been added to ResearchAndMarkets.com s offering.The global market.