Impact of stain variation and color normalization for prognostic predictions in pathology by Siyu Lin, Haowen Zhou, Mark Watson, Ramaswamy Govindan, Richard J. Cote, Changhuei Yang

“…The failure to generalize did not improve even when the tinctorial difference corrections were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.…”
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Investigating the Working Efficiency of Typical Work in High-Altitude Alpine Metal Mining Areas Based on a SeqGAN-GABP Mixed Algorithm by Ning Hua, He Huang, Xinhong Zhang

“…Third, a model based on the sequence generative adversarial network genetic algorithm backpropagation (SeqGAN-GABP) hybrid algorithm was established to predict the trends in the operating efficiency of typical work types in high-altitude alpine metal mining areas. …”
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Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan. by Chuan-Sheng Hung, Chun-Hung Richard Lin, Jain-Shing Liu, Shi-Huang Chen, Tsung-Chi Hung, Chih-Min Tsai

“…Secondly, we introduce a combined model, the Disease Classifier with CTGAN (CTGAN-DC), which integrates DC with Conditional Tabular Generative Adversarial Network (CTGAN) technology to improve data balance and predictive performance further. …”
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Comparison of three recent discrete stochastic inversion methods and influence of the prior choice by Juda, Przemysław, Straubhaar, Julien, Renard, Philippe

“…PoPEx and ESDMA are used with Multiple-point statistics (MPS) as geostatistical engines, and DREAM-ZS is used with a Wasserstein generative adversarial network (WGAN). The three inversion methods are tested on a synthetic example of a pumping test in a fluvial channelized aquifer. …”
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Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network by Yantao Ma, Jie Xue, Xinlong Feng, Jianping Zhao, Junhu Tang, Huaiwei Sun, Jingjing Chang, Longke Yan

“…In this study, we propose a framework that integrates remote sensing data, time series generative adversarial network (TimeGAN), dynamic Bayesian network (DBN), and optimization model to assess crop water productivity and optimize crop planting structure under limited water resources allocation in the Qira oasis. …”
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A multi-stage deep learning network toward multi-classification of polyps in colorectal images by Shilong Chang, Kun Yang, Yucheng Wang, Yufeng Sun, Chaoyi Qi, Wenlong Fan, Ying Zhang, Shuang Liu, Wenshan Gao, Jie Meng, Linyan Xue

“…Facing a significant class imbalance, particularly in the underrepresented categories of villous and serrated adenomas, we employed Generative Adversarial Network Augmentation (GAN-Aug) to synthesize additional images, thereby ensuring a more balanced dataset for training. …”
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Artificial intelligence empowered voice generation for amyotrophic lateral sclerosis patients by Stefano Regondi, Giordana Donvito, Emanuele Frontoni, Milutin Kostovic, Fabio Minazzi, Sébastien Bratières, Massimiliano Filosto, Raffaele Pugliese

“…The AI-generated patient-specific voice is achieved through voice recording, followed by fine-tuning using a Generative Adversarial Network for Efficient and High Fidelity Speech Synthesis (HiFi-GAN), resulting in a model capable of producing speech highly similar to the patient’s own voice, with exceptional expressive and audio quality. …”
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A Transfer Learning Method to Generate Synthetic Synoptic Magnetograms by Xiaoyue Li, Valliappan Senthamizh Pavai, Daria Shukhobodskaia, Mark D. Butala, Luciano Rodriguez, Jasmina Magdalenic, Véronique Delouille

“…Toward this goal, we develop a method called Transfer‐Solar‐GAN which combines a conditional generative adversarial network with a transfer learning approach to overcome training data set limitations. …”
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Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography by Vineeta Das, Furu Zhang, Andrew J. Bower, Joanne Li, Tao Liu, Nancy Aguilera, Bruno Alvisio, Zhuolin Liu, Daniel X. Hammer, Johnny Tam

“…Methods Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. …”
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An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model by Mahan Fakhrooeian, Ali Basem, Mohammad Mahdi Gholami, Nahal Iliaee, Alireza Mohammadi Amidi, Amin Heydarian Hamzehkanloo, Akbar Karimipouya

“…To overcome this problem, the paper presents a learning generative network model, based on the generative adversarial network (GAN) paradigm, to recognize the change in probability values that is maliciously aimed. …”
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GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition by Adnan Saeed, Khurram Shehzad, Shahzad Sarwar Bhatti, Saim Ahmed, Ahmad Taher Azar

“…Additionally, we utilized an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to generate images that balance MRI data and implemented multiple preprocessing techniques to tackle inherent noise in MRI images. …”
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A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging by Luca Vellini, Flaviovincenzo Quaranta, Sebastiano Menna, Elisa Pilloni, Francesco Catucci, Jacopo Lenkowicz, Claudio Votta, Michele Aquilano, Andrea D’Aviero, Martina Iezzi, Francesco Preziosi, Alessia Re, Althea Boschetti, Danila Piccari, Antonio Piras, Carmela Di Dio, Alessandro Bombini, Gian Carlo Mattiucci, Davide Cusumano

“…A conditional Generative Adversarial Network (cGAN) was trained on pre-processed axial paired images. sCTs were validated using mean absolute error (MAE) and mean error (ME) calculated within the patient body. …”
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cigFacies: a massive-scale benchmark dataset of seismic facies and its application by H. Gao, X. Wu, X. Sun, M. Hou, M. Hou, H. Gao, G. Wang, H. Sheng

“…Guided by the graph, we then implement the three strategies of field seismic data curation, knowledge-guided synthesization, and generative adversarial network (GAN)-based generation to construct a benchmark dataset of 8000 diverse samples for five common seismic facies. …”
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Accelerated phase-contrast magnetic resonance imaging with use of resolution enhancement generative adversarial neural network by Manuel A. Morales, Fahime Ghanbari, Ömer Burak Demirel, Jordan A. Street, Tess E. Wallace, Rachel Davids, Jennifer Rodriguez, Scott Johnson, Patrick Pierce, Warren J. Manning, Reza Nezafat

“…Methods: CRISPFlow was built on the super-resolution generative adversarial network. The model was trained and tested (4:1 ratio) using retrospectively identified phase-contrast images from 2020 patients (56 ± 16 years; 1131 men) referred for clinical 3T CMR at a single center from 2018 to 2023. …”
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Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence by Omer Burak Demirel, Fahime Ghanbari, Christopher W. Hoeger, Connie W. Tsao, Adele Carty, Long H. Ngo, Patrick Pierce, Scott Johnson, Kathryn Arcand, Jordan Street, Jennifer Rodriguez, Tess E. Wallace, Kelvin Chow, Warren J. Manning, Reza Nezafat

“…The generative AI model is an image enhancement technique built on the enhanced super-resolution generative adversarial network. The model was trained using balanced steady-state free-precession cine images, readily used for LGE without additional training. …”
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MSH-YOLOv8: Mushroom Small Object Detection Method with Scale Reconstruction and Fusion by YE Dapeng, JING Jun, ZHANG Zhide, LI Huihuang, WU Haoyu, XIE Limin

“…During the data augmentation phase, a generative adversarial network (GAN) was utilized for resolution reconstruction of low-resolution images, thereby preserving semantic quality as much as possible. …”
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