2,058 research outputs found
The Effect of Training Dataset Size on SAR Automatic Target Recognition Using Deep Learning
Synthetic aperture radar (SAR) is an effective remote sensor for target detection and recognition. Deep learning has a great potential for implementing automatic target recognition based on SAR images. In general, Sufficient labeled data are required to train a deep neural network to avoid overfitting. However, the availability of measured SAR images is usually limited due to high cost and security in practice. In this paper, we will investigate the relationship between the recognition performance and training dataset size. The experiments are performed on three classifiers using MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset. The results show us the minimum size of the training set for a particular classification accuracy
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model
Head and neck (H&N) cancers are among the most prevalent types of cancer
worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management.
Recently, the diffusion model has demonstrated remarkable performance in
various image-generation tasks. In this work, we proposed a 3D diffusion model
to accurately perform H&N tumor segmentation from 3D PET and CT volumes. The 3D
diffusion model was developed considering the 3D nature of PET and CT images
acquired. During the reverse process, the model utilized a 3D UNet structure
and took the concatenation of PET, CT, and Gaussian noise volumes as the
network input to generate the tumor mask. Experiments based on the HECKTOR
challenge dataset were conducted to evaluate the effectiveness of the proposed
diffusion model. Several state-of-the-art techniques based on U-Net and
Transformer structures were adopted as the reference methods. Benefits of
employing both PET and CT as the network input as well as further extending the
diffusion model from 2D to 3D were investigated based on various quantitative
metrics and the uncertainty maps generated. Results showed that the proposed 3D
diffusion model could generate more accurate segmentation results compared with
other methods. Compared to the diffusion model in 2D format, the proposed 3D
model yielded superior results. Our experiments also highlighted the advantage
of utilizing dual-modality PET and CT data over only single-modality data for
H&N tumor segmentation.Comment: 28 pages, 5 figure
Crescent Waves in Optical Cavities
We theoretically and experimentally generate stationary crescent surface
solitons pinged to the boundary of a micro-structured vertical cavity surface
emission laser by using the intrinsic cavity mode as a background potential.
Instead of a direct transition from linear to nonlinear cavity modes, we
demonstrate the existence of a symmetry-breaking crescent waves without any
analogs in the linear limit. Our results provide an alternative and general
method to control lasing characteristics as well as to study optical surface
waves.Comment: 3 figure
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