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Using the Regression Slope of Training Loss to Optimize Chest X-ray Generation in Deep Convolutional Generative Adversarial Networks.
Cureus
January 2025
Department of Critical Care Medicine, Jen Ho Hospital, Show Chwan Health Care System, Changhua, TWN.
Diffusion models, variational autoencoders, and generative adversarial networks (GANs) are three common types of generative artificial intelligence models for image generation. Among these, GANs are the most frequently used for medical image generation and are often employed for data augmentation in various studies. However, due to the adversarial nature of GANs, where the generator and discriminator compete against each other, the training process can sometimes end with the model unable to generate meaningful images or even producing noise.
View Article and Find Full Text PDFLocal structure of amorphous sulfur in carbon-sulfur composites for all-solid-state lithium-sulfur batteries.
Commun Chem
January 2025
Graduate School of Natural Science and Technology, Shimane University, Nishikawatsu-cho, Matsue, Shimane, Japan.
All-solid-state (ASS) batteries are a promising solution to achieve carbon neutrality. ASS lithium-sulfur (Li-S) batteries stand out due to their improved safety, achieved by replacing organic solvents, which are prone to leakage and fire, with solid electrolytes. In addition, these batteries offer the benefits of higher capacity and the absence of rare metals.
View Article and Find Full Text PDFDeep generative modeling of annotated bacterial biofilm images.
NPJ Biofilms Microbiomes
January 2025
Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow, 119991, Russia.
Biofilms are critical for understanding environmental processes, developing biotechnology applications, and progressing in medical treatments of various infections. Nowadays, a key limiting factor for biofilm analysis is the difficulty in obtaining large datasets with fully annotated images. This study introduces a versatile approach for creating synthetic datasets of annotated biofilm images with employing deep generative modeling techniques, including VAEs, GANs, diffusion models, and CycleGAN.
View Article and Find Full Text PDFVerdazyl radical polymers for advanced organic spintronics.
Nat Commun
January 2025
Charles D. Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA.
Spin currents have long been suggested as a potential solution to addressing circuit miniaturization challenges in the semiconductor industry. While many semiconducting materials have been extensively explored for spintronic applications, issues regarding device performance, materials stability, and efficient spin current generation at room temperature persist. Nonconjugated paramagnetic radical polymers offer a unique solution to these challenges.
View Article and Find Full Text PDFDECT sparse reconstruction based on hybrid spectrum data generative diffusion model.
Comput Methods Programs Biomed
January 2025
Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; School of Computer Science and Engineering, Southeast University, Nanjing, China.
Purpose: Dual-energy computed tomography (DECT) enables the differentiation of different materials. Additionally, DECT images consist of multiple scans of the same sample, revealing information similarity within the energy domain. To leverage this information similarity and address safety concerns related to excessive radiation exposure in DECT imaging, sparse view DECT imaging is proposed as a solution.
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