Latent-image fading can, however, be eliminated by storing the coated plates in dry nitrogen or carbon dioxide. These methods should prove particularly valuable when exposures are made to isotopes of half-life longer than 30 days, and for improving the quantitative reproducibility of autoradiographic...
RC Ray,GWW Stevens - 《British Journal of Radiology》 被引量: 0发表: 2014年 Reciprocity failure and latent-image fading in autoradiography. Br J Radiol. 1953 Jul;26(307):362-7. RC Ray,GWW Stevens - 《Br J Radiol》 被引量: 0发表: 2014年 Latent Image Theory and its Experimental Applic...
Although generative adversarial networks (GANs) can produce large datasets, their limited diversity and fidelity have been recently addressed by denoising diffusion probabilistic models, which have demonstrated superiority in natural image synthesis. In
,... - 《Investigative Radiology》 被引量: 1发表: 1981年 Color photographic silver halide light-sensitive material *Relative values of the reciprocals of exposure amounts necessary for obtaining a density of fog + 0.1. Separately, Samples I, II, III, A and B were processed by changing the...
We propose style-subnets-assisted generative latent bank for large-factor super-resolution (SGSR) trained with registered medical image datasets. Pre-trained generative models named generative latent bank (GLB), which stores rich image priors, can be applied in SR to generate realistic and faithful ...
Household contacts were evaluated for active TB using symptoms, signs, and chest radiology. All chest X-rays were examined by two radiologists, blinded to clinical details. A scale of severity was assigned as described by Petruccioli et al.; 0: normal chest X-ray; 1: mild grade; 2: ...
In a second phase, the medical image is indexed while recovering areas of interest which are invariant to change in scale, light and tilt. To annotate a new medical image, we use the approach of "bagof-words" to recover the feature vector. Indeed, we use the vector space model to ...
Latent shape image learning via disentangled representation for cross-sequence image registration and segmentationdoi:10.1007/s11548-022-02788-9Wu, JiongYang, QiZhou, ShuangInternational Journal of Computer Assisted Radiology & Surgery
Unsupervised learn- ing in radiology using novel latent variable models. In 2005 IEEE Computer Society Con- ference on Computer Vision and Pattern Recognition, volume 2, pages 854-859. IEEE, 2005.L. Carrivick, S. Prabhu, P. Goddard, and J. Rossiter. Un- supervised learning in radiology ...
Exposures below this optimum give insufficient charge densities for subsequent development, while exposures above it degrade the image and eventually result in a uniform foil electret charged to its maximum theoretical value.Fallone B. G.Departments of Radiation Oncology and Diagnostic RadiologyPodgorsak ...