![]() ![]() Jin, D., Xu, Z., Tang, Y., Harrison, A.P., Mollura, D.J.: CT-realistic lung nodule simulation from 3D conditional generative adversarial networks for robust lung segmentation. In: 2019 International Conference on 3D Vision (3DV), pp. Han, C., et al.: Synthesizing diverse lung nodules wherever massively: 3D multi-conditional GAN-based CT image augmentation for object detection. In: Advances in Neural Information Processing Systems, pp. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. Goodfellow, I., et al.: Generative adversarial nets. International Society for Optics and Photonics (2019) In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. Gao, C., Clark, S., Furst, J., Raicu, D.: Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans Conditional generative adversarial networks.The benchmark with LIDC-IDRI dataset showed that the lung nodule synthesis quality is comparable to 3D generative models in the Visual Turing test with lower computation costs. Nodule feature information is considered as input in the latent space in U-Net to generate targeted synthetic nodules. The proposed model can generate 2D synthetic slices sequentially with U-Net architecture and bi-directional convolutional long short-term memory for nodule reconstruction and injection. We propose a novel CT generation model using attribute-guided generative adversarial networks. However, the synthesis still has limitations, such as spatial discontinuity, background changes, and vast computational cost. Three-dimensional conditional generative adversarial networks generate lung nodule synthesis, controlling malignancy and benignancy. With this approach, our main goal is to conclude the first step of a further multicenter study to propose the standardization of detection and quantification of Leishmania.Synthetic CT images are used in data augmentation methods to tackle small and fragmented training datasets in medical imaging. Following the methodology proposed herein, the results indicate the use of both qPCR assays, 18S rDNA and HSP70, to achieve a very good net sensitivity (98.5%) and specificity (100%), performing simultaneous or sequential testing, respectively. Additionally, we validated reactions by assaying 88 samples from patients presenting different clinical forms of leishmaniasis (cutaneous, mucosal, recent and old lesions), representing the diversity found in Brazil's Amazon Region. In this sense, the purpose of the present work is to compare the performance of qPCR using two commonly used targets (18S rDNA and HSP70) with an internal control (RNAse P) in multiplex reactions. Moreover, a proper validation by the assay by the use of clinical samples is still required. Although many studies have been already published addressing the use of this technique, an improvement on these methodologies, including an analytical validation, standardization and data association is demanded. Molecular approaches, specially based on Real Time PCR (qPCR) technique, has been widely used to detect Leishmania infection and to quantify parasite load, once it is a simple, rapid and sensitive methodology, capable to detect low parasite concentrations and less prone to variability. The sensitivity of the method varies, and factors such as collection procedures interfere. Currently, ATL diagnosis is mainly made by parasite detection by microscopy. As a neglected disease, much effort is still needed in treatment and diagnosis. Leishmaniasis is a worldwide neglected disease, encompassing asymptomatic infections and different clinical forms, such as American Tegumentary Leishmaniasis (ATL) which is part of the complex of diseases caused by protozoan parasites from Leishmania genus, transmitted by sand fly vectors. ![]()
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