Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology by Yinhu Gao, Peizhen Wen, Yuan Liu, Yahuang Sun, Hui Qian, Xin Zhang, Huan Peng, Yanli Gao, Cuiyu Li, Zhangyuan Gu, Huajin Zeng, Zhijun Hong, Weijun Wang, Ronglin Yan, Zunqi Hu, Hongbing Fu

“…However, the scale and quality of data across different studies vary widely, and the generalizability of models to multi-center, multi-device environments remains to be verified. …”
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Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology by Petr Kuritcyn, Rosalie Kletzander, Sophia Eisenberg, Thomas Wittenberg, Volker Bruns, Katja Evert, Felix Keil, Paul K. Ziegler, Katrin Bankov, Peter Wild, Markus Eckstein, Arndt Hartmann, Carol I. Geppert, Michaela Benz

“…Therefore, we investigated the influence of prototypes originating from images from different scanners and evaluated their performance also on the multiscanner database. …”
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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

“…Then, DL models were constructed to predict percentage changes in symptom scores using genetic variants selected based on four different genome-wide P thresholds (E-02, E-03, E-04, E-05) as inputs. …”
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Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review by Seng Hansun, Ahmadreza Argha, Ivan Bakhshayeshi, Arya Wicaksana, Hamid Alinejad-Rokny, Greg J Fox, Siaw-Teng Liaw, Branko G Celler, Guy B Marks

“…AI performance across different biomarker types showed mean accuracies of 92.45% (SD 7.83%), 89.03% (SD 8.49%), and 84.21% (SD 0%); mean AUCs of 94.47% (SD 7.32%), 88.45% (SD 8.33%), and 88.61% (SD 5.9%); mean sensitivities of 93.8% (SD 6.27%), 88.41% (SD 10.24%), and 93% (SD 0%); and mean specificities of 94.2% (SD 6.63%), 85.89% (SD 14.66%), and 95% (SD 0%) for radiographic, molecular/biochemical, and physiological types, respectively. …”
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Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences by Janet M. Baker, Peter Cariani

“…A hypothetical example illustrates how a succession of different oscillation carriers (gamma, beta, alpha, theta, and delta) could communicate and propagate (broadcast) information sequentially through a neural hierarchy of speech and language processing stages. …”
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Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model by Li-Hsin Cheng, Xiaowu Sun, Charlie Elliot, Robin Condliffe, David G. Kiely, Samer Alabed, Andrew J. Swift, Rob J. van der Geest, David G Kiely, Lisa Watson, Iain Armstrong, Catherine Billings, Athanasios Charalampopoulos, Robin Condliffe, Charlie Elliot, Abdul Hameed, Neil Hamilton, Judith Hurdman, Allan Lawrie, Robert A Lewis, Smitha Rajaram, Alex Rothman, Andy J. Swift, Steven Wood, AA Roger Thompson, Jim Wild

“…Methods: We constructed an explainable convolutional neural network (CNN) taking cardiac MR cine series from four different views as input to predict mean pulmonary artery pressure (mPAP). …”
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Intelligent Forecasting for Solar Flares Using Magnetograms from SDO/SHARP, SDO/HMI, and ASO-S/FMG by Xuebao Li, Hongwei Ye, Yanfang Zheng, Ting Li, Jiaben Lin, Shunhuang Zhang, Pengchao Yan, Yongshang Lv, Noraisyah Mohamed Shah, Xuefeng Li, Xiaotian Wang, Yingbo Liu, Rui Wang, Jinfang Wei, Changtian Xiang, Honglei Jin

“…Furthermore, we investigate the generalization capability of the models by using multisource data collected within the same period, as well as single-source data gathered across different periods. This is the first time that we utilize ASO-S/FMG data for flare forecasting. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…In this process, the loss is calculated based on the differences in length, width, and diagonal between the detection and ground-truth boxes, and batch normalization (BN) layer sparsification is applied for convolutional channel filtering. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…The impact of the MSDA placement on model performance was analyzed by adding it at different positions in the Neck layer, and relevant experiments were designed to verify this. …”
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“Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

“…At the settlement scale, the Mask Region-based Convolutional Neural Network (Mask R-CNN) deep learning model is utilized to identify architectural spatial features, categorizing three typical building types: traditional pitched-roof buildings, uniformly planned flat-roof buildings, and color steel plate-modified structures. …”
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Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine Learning and Simulations by Jonah C. Rose, Paul Torrey, Francisco Villaescusa-Navarro, Mariangela Lisanti, Tri Nguyen, Sandip Roy, Kassidy E. Kollmann, Mark Vogelsberger, Francis-Yan Cyr-Racine, Mikhail V. Medvedev, Shy Genel, Daniel Anglés-Alcázar, Nitya Kallivayalil, Bonny Y. Wang, Belén Costanza, Stephanie O’Neil, Cian Roche, Soumyodipta Karmakar, Alex M. Garcia, Ryan Low, Shurui Lin, Olivia Mostow, Akaxia Cruz, Andrea Caputo, Arya Farahi, Julian B. Muñoz, Lina Necib, Romain Teyssier, Julianne J. Dalcanton, David Spergel

“…The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. …”
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An improved CNN model in image classification application on water turbidity by Ying Nie, Yuqiang Chen, Jianlan Guo, Shufei Li, Yu Xiao, Wendong Gong, Ruirong Lan

“…Due to the subtle changes in water turbidity images, the differences captured are often too subtle to be classified. …”
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Defect Diagnosis of Photovoltaic Module Visible Light Images Under Imbalanced Sample Conditions by Huiqing Rao, Qiong Li, Long Chen, Sha Jin, Yong Lu, Zhiguang Li

“…Firstly, in the DCGAN model, fully connected layers are incorporated into both the generator and the discriminator, and the transpose convolutional layers and convolutional layers are replaced with residual blocks. …”
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Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence by Liliana Ibeth Barbosa-Santillán, Bertha Patricia Guzman-Velazquez, Ma. Teresa Orozco-Aguilera, Leticia Flores-Pulido

“…Weapon detection is a complex task due to the variability in object exposures and differences in weapon shapes, sizes, orientations, colors, and image capture methods. …”
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Cross-Scenario Subdomain Adaptive Displacement Anomaly Detection in Dams by Yu Wang, Guohua Liu

“…To overcome the challenges of limited data, domain distribution differences, and the need for retraining in unsupervised learning methods for cross-scenario anomaly detection in dams, this study introduces a novel approach; the Temporal Displacement Subdomain Adaptation Network (TDSAN) combines temporal convolutional networks with subdomain adaption. …”
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An integrated approach for advanced vehicle classification. by Rui Liu, Shiyuan Wen, Yufei Xing

“…The DWAN model can identify and accurately classify subtle features and differences in automotive images, resulting in better classification results for the automotive fine-grained category. …”
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Listening deeper: neural networks unravel acoustic features in preterm infant crying by Yuta Shinya, Taiji Ueno, Masahiko Kawai, Fusako Niwa, Seiichi Tomotaki, Masako Myowa

“…Our convolutional neural network models showed high accuracy in classifying gestational groups (92.4%) and in estimating the relative and continuous differences in GA (r = 0.73; p < 0.0001), outperforming previous studies. …”
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An ensemble-based 3D residual network for the classification of Alzheimer's disease. by Xiaoli Yang, Jiayi Zhou, Chenchen Wang, Xiao Li, Jiawen Wang, Angchao Duan, Nuan Du

“…Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtle imaging differences. Furthermore, differentiating early MCI (EMCI) from late MCI (LMCI) is also important for interventions. …”
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Pulmonary and Cardiorenal Cyclooxygenase-1 (COX-1), -2 (COX-2), and Microsomal Prostaglandin E Synthase-1 (mPGES-1) and -2 (mPGES-2) Expression in a Hypertension Model by Zaher A. Radi, Robert Ostroski

“…Renal expression of COX-1 was the highest in the distal convoluted tubules, cortical collecting ducts, and medullary collecting ducts; while proximal convoluted tubules lacked COX-1 expression. …”
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Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…The preprocessing module, composed of Wavelet Transform (WT) and the Maximum Information Coefficient (MIC) algorithms, was used to perform noise reduction and parameter correlation analysis on the raw data, thereby generating enhanced inputs. The Convolutional Neural Network (CNN) integrated with a channel-wise attention mechanism explored parameter weight differences and extracted local data features. …”
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