Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers by Lu Jiang, Di Xu, Qifan Xu, Arion Chatziioannou, Keisuke S. Iwamoto, Susanta Hui, Ke Sheng

“…Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. …”
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FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US by Musa Turkan, Emre Dandil, Furkan Erturk Urfali, Mehmet Korkmaz

“…To evaluate FetalMovNet, we construct a new dataset containing fetal movements in US across seven different anatomical structures-head, body, arm, hand, heart, leg, and foot. …”
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Multi-scale extreme climate disaster prediction model integrated with ConvLSTM: taking rainstorm and flood disaster as an example by Lei He, Yunhao He, Yongqiang Xia, Yuxia Li, Bin Liu, Siqi Zhang, Cunjie Zhang

“…Firstly, four disaster indicators (population disaster index, housing disaster index, agricultural disaster index and economic disaster index) were introduced to reflect different losses, which could form a comprehensive disaster index to quantify the overall loss degree; Second, with raster data and VGGNet, a lightweight regression convolutional neural network model VGG-Light was proposed to solve these problem; Third, focused on impact of precipitation on disaster situations, the ConvLSTM module was used to capture the spatiotemporal characteristics of precipitation data, and then the TSVGG-Light model was presented for feature fusion. …”
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ViViT-Prob: A Radar Echo Extrapolation Model Based on Video Vision Transformer and Spatiotemporal Sparse Attention by Yunan Qiu, Bingjian Lu, Wenrui Xiong, Zhenyu Lu, Le Sun, Yingjie Cui

“…The model takes historical sequences as input and initially maps them into a fixed-dimensional vector space through 3D convolutional patch encoding. Subsequently, a multi-head spatiotemporal fusion module with sparse attention encodes these vectors, effectively capturing spatiotemporal relationships between different regions in the sequences. …”
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Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature by Wang Benlin, Yu Qinglin, Wang Zuo, Li Weitao, Wang Yong, Liu Huan, Gu Shuangxi, Zhang Lingling, Lv Dong

“…To address this, we proposed a jointly fused convolutional neural network for spectral super-resolution (JF-CNNSSR), leveraging spectral reflectance variations across different land cover types. …”
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Tree semantic segmentation from aerial image time series by Venkatesh Ramesh, Arthur Ouaknine, David Rolnick

“…Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of forests. …”
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RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect by Youngho Min, Young Rock Kim, YunKyong Hyon, Taeyoung Ha, Sunju Lee, Jinwoo Hyun, Mi Ra Lee

“…In this investigation, we applied five different smoothing time window lengths to evaluate the effect of mitigating short-term fluctuations on the predictive performance of the models. …”
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APO-CViT: A Non-Destructive Estrus Detection Method for Breeding Pigs Based on Multimodal Feature Fusion by Jinghan Cai, Wenzheng Liu, Tonghai Liu, Fanzhen Wang, Zhihan Li, Xue Wang, Hua Li

“…By integrating the Vision Transformer and convolutional neural networks, the model extracted and fused features from multimodal data. …”
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Identification of Alzheimer’s disease brain networks based on EEG phase synchronization by Jiayi Cao, Bin Li, Xiaoou Li

“…Abstract Objective Using the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer’s disease (AD) and to assist in the early diagnosis of Alzheimer’s disease. …”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…This indicates that based on the differences in phosphorescence scattering signals of different morphologies in water bodies, the use of hyperspectral remote sensing and the CNN-RF model can effectively extract PP spatiotemporal information, strengthen the learning capability of multiscale characteristics, and contribute to the improvement of the precision of estimating PP concentration, which could provide an innovative approach for determining the degree of eutrophication of lake water bodies.…”
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Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning by Hitomi Nakamura, Motohisa Fukuda, Akiko Matsuda, Naohiko Makino, Hirohito Kimura, Yu Ohtaki, Yoshihito Nawa, Soushi Oyama, Yuya Suzuki, Toshikazu Kobayashi, Tetsuya Ishizawa, Yasuharu Kakizaki, Yoshiyuki Ueno

“…We divided patients into five groups according to different factors for 5‐fold cross‐validation, where the ordered and balanced datasets were created for the performance evaluations. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…By leveraging healthy images to learn the normal skeletal distribution, the approach reduces the dependency on labeled fracture data and effectively addresses the challenges posed by limited pediatric datasets. Datasets from two different hospitals were used, with data augmentation techniques applied during both training and validation. …”
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A Violet‐Light‐Responsive ReRAM Based on Zn2SnO4/Ga2O3 Heterojunction as an Artificial Synapse for Visual Sensory and In‐Memory Computing by Saransh Shrivastava, Wei‐Sin Dai, Stephen Ekaputra Limantoro, Hans Juliano, Tseung‐Yuen Tseng

“…Classification of three‐channeled images corrupted with different levels (0.15–0.9) of Gaussian noise is achieved by simulating a convolutional neural network (CNN). …”
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Soil Condition Classification Based on Natural Water Content Using Computer Vision Technique by Mark Miller, Yong Fang, Yubo Wang, Sergey Kharitonov, Vladimir Akulich

“…First, laboratory soil tests were carried out, and the relationship between the amount of torque on the screw conveyor and the moisture content of the soil was established; photographs of the soil at different conditions were taken at each step of the experiment. …”
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Research on insider threat detection based on personalized federated learning and behavior log analysis by Xiaoyun Ye, Faqin Luo, Huangrongbin Cui, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

“…Drawing on the DeepInsight concept, we convert different data types into image formats for use with Convolutional Neural Networks (CNNs) to train insider threat detection models. …”
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Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll <i>a</i> concentration in the Black Sea by A. Barth, J. Brajard, A. Alvera-Azcárate, B. Mohamed, C. Troupin, J.-M. Beckers

“…They are however affected by clouds (among others), which severely reduce their spatial coverage. Different methods have been proposed in the literature to reconstruct missing data in satellite observations. …”
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Joint feature representation optimization and anti-occlusion for robust multi-vessel tracking in inland waterways by Shenjie Zou, Jin Liu, Xiliang Zhang, Zhongdai Wu, Jing Liu, Bing Han

“…Finally, a bidirectional feature pyramid network (BiFPN) is utilized to fuse vessel appearance features from different scales, enhancing the capability to learn cross-scale features of vessels to some extent. …”
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Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition by Md. Zasim Uddin, Kamrul Hasan, Md Atiqur Rahman Ahad, Fady Alnajjar

“…The consolidated features from both modules enhance CVGR performance, even amidst challenging covariates such as different carried objects and clothing variations, along with uncontrolled walking patterns in the wild. …”
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Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches by Saud Yonbawi, Adil Afzal, Muhammad Yasir, Muhammad Rizwan, Natalia Kryvinska

“…A comprehensive evaluation involving Multilayer Perceptron(MLP), and Convolutional Neural Networks (CNN) models has been executed, uncovering that CNN conspicuously outshines the MLP model.…”
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Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction by Fared Farag, Trevis D. Huggins, Jeremy D. Edwards, Anna M. McClung, Ahmed A. Hashem, Jason L. Causey, Emily S. Bellis

“…Here, we investigate the ability of various machine learning (ML) approaches to improve yield prediction accuracy in new environments from multispectral timeseries imagery acquired on a set of rice (Oryza sativa L.) experiments with different management treatments and varieties. We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. …”
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