An Arctic sea ice concentration data record on a 6.25 km polar stereographic grid from 3 years of Landsat-8 imagery by H.-S. Jung, S.-M. Lee, S.-M. Lee, J.-H. Kim, K. Lee

“…The vast amount of Landsat-8 SIC generated in this study may also be used to train deep-learning models for the estimation of Arctic SIC coverage. …”
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A comprehensive environmental index for monitoring ecological quality of typical alpine wetlands in Central Asia by Jiudan Zhang, Junli Li, Changming Zhu, Anming Bao, Amaury Frankl, Philippe De Maeyer, Tim Van de Voorde

“…This study employed a deep-learning semantic segmentation model to map the structural changes of the Bayanbulak alpine wetland using Landsat imagery from 1977 to 2022. …”
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Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico by O. Zavala-Romero, O. Zavala-Romero, A. Bozec, E. P. Chassignet, J. R. Miranda, J. R. Miranda

“…<p>Deep learning models have demonstrated remarkable success in fields such as language processing and computer vision, routinely employed for tasks like language translation, image classification, and anomaly detection. …”
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Abdominal synthetic CT generation for MR-only radiotherapy using structure-conserving loss and transformer-based cycle-GAN by Chanwoong Lee, Chanwoong Lee, Young Hun Yoon, Young Hun Yoon, Young Hun Yoon, Jiwon Sung, Jun Won Kim, Yeona Cho, Jihun Kim, Jaehee Chun, Jin Sung Kim, Jin Sung Kim, Jin Sung Kim

“…PurposeRecent deep-learning based synthetic computed tomography (sCT) generation using magnetic resonance (MR) images have shown promising results. …”
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scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics by Vishakha Gautam, Subhadeep Duari, Saveena Solanki, Mudit Gupta, Aayushi Mittal, Sakshi Arora, Anmol Aggarwal, Anmol Kumar Sharma, Sarthak Tyagi, Rathod Kunal Pankajbhai, Arushi Sharma, Sonam Chauhan, Shiva Satija, Suvendu Kumar, Sanjay Kumar Mohanty, Juhi Tayal, Nilesh Kumar Dixit, Debarka Sengupta, Anurag Mehta, Gaurav Ahuja

“…Summary: Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. …”
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Contrastive learning with transformer for adverse endpoint prediction in patients on DAPT post-coronary stent implantation by Fang Li, Zenan Sun, Ahmed abdelhameed, Tiehang Duan, Laila Rasmy, Xinyue Hu, Jianping He, Yifang Dang, Jingna Feng, Jianfu Li, Yichen Wang, Tianchen Lyu, Naomi Braun, Si Pham, Michael Gharacholou, DeLisa Fairweather, Degui Zhi, Jiang Bian, Cui Tao

“…We benchmarked model performance against three cutting-edge deep learning-based survival models, i.e., DeepSurv, DeepHit, and SurvTrace.ResultsThe final cohort comprised 19,713 adult patients who underwent DES implantation with more than 1 month of records after coronary stenting. …”
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Utilisation of ChatGPT and other Artificial Intelligence tools among medical faculty in Uganda: a cross-sectional study [version 2; peer review: 1 approved, 2 approved with reserva... by Job Kuteesa, Faith Oguttu, Ian Guyton Munabi, Aloysius Gonzaga Mubuuke, Sarah Kiguli, Ritah Nantale, David Mukunya, Elizabeth Ajalo, Frank Kayemba, Jonathan Babuya, Kennedy Pangholi, Amelia Margaret Namiiro, Suzan Langoya Akuu, Steven Ekak, Ronald Tweheyo, Kirsten Nantongo, Brenda Nakitto, Milton W. Musaba, Joseph Luwaga Mpagi

“…Background ChatGPT is a large language model that uses deep learning techniques to generate human-like texts. …”
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Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types by Jeanne Shen, Sergio Pereira, Chan-Young Ock, Yung-Jue Bang, Seulki Kim, Sehhoon Park, Se-Hoon Lee, George A Fisher, Young Kwang Chae, Yoon-La Choi, Jin-Haeng Chung, Tony S K Mok, Leeseul Kim, Jun-Eul Hwang, Gahee Park, Sanghoon Song, Seunghwan Shin, Yoojoo Lim, Wonkyung Jung, Heon Song, Hyojin Kim, Taebum Lee, Sukjun Kim, Chang Ho Ahn, Seokhwi Kim, Ben W Dulken, Stephanie Bogdan, Maggie Huang, Chiyoon Oum, Siraj M. Ali

“…Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types.Methods Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. …”
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Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging by Oliver Lester Saldanha, Jiefu Zhu, Gustav Müller-Franzes, Zunamys I. Carrero, Nicholas R. Payne, Lorena Escudero Sánchez, Paul Christophe Varoutas, Sreenath Kyathanahally, Narmin Ghaffari Laleh, Kevin Pfeiffer, Marta Ligero, Jakob Behner, Kamarul A. Abdullah, Georgios Apostolakos, Chrysafoula Kolofousi, Antri Kleanthous, Michail Kalogeropoulos, Cristina Rossi, Sylwia Nowakowska, Alexandra Athanasiou, Raquel Perez-Lopez, Ritse Mann, Wouter Veldhuis, Julia Camps, Volkmar Schulz, Markus Wenzel, Sergey Morozov, Alexander Ciritsis, Christiane Kuhl, Fiona J. Gilbert, Daniel Truhn, Jakob Nikolas Kather

“…Results Upon systematically benchmarking various weakly supervised two-dimensional (2D) and three-dimensional (3D) deep learning (DL) methods, we find that the 3D-ResNet-101 demonstrates superior performance. …”
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Detection Method of Apple Alternaria Leaf Spot Based on Deep-Semi-NMF by FU Zhuojun, HU Zheng, DENG Yangjun, LONG Chenfeng, ZHU Xinghui

“…A comparative analysis was conducted with several other detection methods, including GRX (Reed-Xiaoli detector), LRX (Local Reed-Xiaoli detector), CRD (Collaborative-Representation-Based Detector), LSMAD (LRaSMD-Based Mahalanobis Distance Detector), and the deep learning model Unet. The results demonstrated that DSNMFMAD exhibited superior performance in the laboratory environment. …”
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Detection Method of Effective Tillering of Rice in Field Based on Lightweight Ghost-YOLOv8 and Smart Phone by CUI Jiale, ZENG Xiangfeng, REN Zhengwei, SUN Jian, TANG Chen, YANG Wanneng, SONG Peng

“…Combined with the deep learning model, a high-throughput and low-cost mobile phone App for effective tiller detection in rice was developed to solve the practical problems of effective tiller investigation in rice under field conditions.…”
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Lightweight Daylily Grading and Detection Model Based on Improved YOLOv10 by JIN Xuemeng, LIANG Xiyin, DENG Pengfei

“…AKVanillaNet combines AKConv (adaptive kernel convolution) with VanillaNet's deep learning and shallow inference mechanisms. The second convolutional layer in VanillaNet was replaced with AKConv, and AKConv was merged with standard convolution layers at the end of the training phase to optimize the model for capturing the unique shape characteristics of dried daylilies. …”
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Extended depth-of-field microscopic imaging for intact histological samples via self-calibrated diplane network by Yuezhi He, Xu Yang, Shiwei Ye, Zonghua Wen, Peng Liu, Hui Li, Feng Xiang, Jiuling Liao, Yizhou Tan, Haoyong Ning, Junjie Zou, Guilu He, Long Zeng, Yanwu Guo, Hui Li, Ying Gu, Bo Wang, Wei Zheng

“…In this study, we propose deep-learning-powered, extended-DOF, dark-field reflectance ultraviolet microscopy (DE-DRUM) for rapid and large-DOF imaging of surgically resected tissues. …”
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Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context by Jiesuck Park, Jiyeon Kim, Jaeik Jeon, Yeonyee E. Yoon, Yeonggul Jang, Hyunseok Jeong, Youngtaek Hong, Seung-Ah Lee, Hong-Mi Choi, In-Chang Hwang, Goo-Yeong Cho, Hyuk-Jae Chang

“…Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation. …”
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Integrated Framework and Technical Path for Multi-level Nested Assessment of Landscape Character by Yuncai WANG, Qizhen DONG

“…By combining multi-scale segmentation and spatial clustering techniques of deep learning, a technical path for multi-level nested landscape character assessment is constructed as a new idea for characterizing the local characteristics of landscape at multiple scales.ConclusionIn the process of developing landscape character assessment systems, there have been numerous methodological systems. …”
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Micro-expression recognition based on self-supervised masked optical flow(基于自监督掩码光流的人脸微表情识别) by 刘晓宇(LIU Xiaoyu), 谢志华(XIE Zhihua), 周志武(ZHOU Zhiwu)

“…The micro-expression recognition method based on SMOF achieves a UF1 score of 0.861 4 and a UAR score of 0.871 6, outperforming other deep learning methods for micro-expression recognition. …”
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BDC-DAL: A new algorithm using blueprint difference convolution and dual auxiliary supervision for lightweight face anti-spoofing(BDC-DAL：基于蓝图差分卷积和双重辅助监督的轻量化人脸活体检测算法)... by 叶继华(YE Jihua), 梁芳昕(LIANG Fangxin), 王超(WANG Chao), 肖顺杰(XIAO Shunjie), 宗义(ZONG Yi), 江爱文(JIANG Aiwen)

“…The test results are compared with those by performing the current mainstream deep learning face anti-spoofing algorithms and other lightweight network models. …”
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New rectum dose surface mapping methodology to identify rectal subregions associated with toxicities following prostate cancer radiotherapy by Artemis Bouzaki, Dylan Green, Marcel van Herk, Jane Shortall, Tanuj Puri, Sarah Kerns, David Azria, Marrie-Pierre Farcy-Jacquet, Jenny Chang-Claude, Ananya Choudhury, Alison Dunning, Maarten Lambrecht, Barbara Avuzzi, Dirk De Ruysscher, Petra Seibold, Elena Sperk, Christopher Talbot, Ana Vega, Liv Veldeman, Adam Webb, Barry Rosenstein, Catharine M. West, Eliana Gioscio, Tiziana Rancati, Eliana Vasquez Osorio, Alan McWilliam

“…Materials and methods: Data of 1,048 patients with prostate cancer from the REQUITE study were used. Deep learning based automatic segmentations were generated to ensure consistency. …”
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Navigating Ethical Dilemmas Of Generative AI In Medical Writing by Qurrat Ulain Hamdan, Waleed Umar, Mahnoor Hasan

“…Generative AI in Medical Writing Generative AI tools or “chatbots” combine the adaptive learning capabilities of deep learning algorithms and natural language processing, resulting in a virtual assistant or aide that is capable of answering queries, following commands, and improving its responses according to the vast data available on the Internet in addition to user responses.3 This has allowed the accomplishment of various complex tasks within seconds that would otherwise require hours of trial and error. …”
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Rice Leaf Disease Image Enhancement Based on Improved CycleGAN by YAN Congkuan, ZHU Dequan, MENG Fankai, YANG Yuqing, TANG Qixing, ZHANG Aifang, LIAO Juan

“…This addition enabled the deep learning model to more accurately measure perceptual differences between the generated images and real images, thereby guiding the network towards producing higher-quality samples. …”
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