AFN-Net: Adaptive Fusion Nucleus Segmentation Network Based on Multi-Level U-Net by Ming Zhao, Yimin Yang, Bingxue Zhou, Quan Wang, Fu Li

“…This DSCOM effectively preserves high-resolution information and improves the segmentation accuracy of small targets and boundary regions through multi-level convolution operations and channel optimization. Finally, we proposed an Adaptive Fusion Loss Module (AFLM) that effectively balances different lossy targets by dynamically adjusting weights, thereby further improving the model’s performance in segmentation region consistency and boundary accuracy while maintaining classification accuracy. …”
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BDSER-InceptionNet: A Novel Method for Near-Infrared Spectroscopy Model Transfer Based on Deep Learning and Balanced Distribution Adaptation by Jianghai Chen, Jie Ling, Nana Lei, Lingqiao Li

“…Traditional modeling methods exhibit certain limitations in handling these factors, making it difficult to achieve effective adaptation across different scenarios. Specifically, data distribution shifts and mismatches in multi-scale features hinder the transferability of models across different crop varieties or instruments from different manufacturers. …”
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FruitQuery: A lightweight query-based instance segmentation model for in-field fruit ripeness determination by Ziang Zhao, Yulia Hicks, Xianfang Sun, Chaoxi Luo

“…FruitQuery runs in an end-to-end way and incorporates the convolution and Transformer to capture fine-grained features related to different fruits at different ripeness stages.Extensive experiments on the combined fruit dataset demonstrate that our FruitQuery achieves the highest average precision of 67.02 with only 14.08M parameters, outperforming 13 state-of-the-art models with 33 variants. …”
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Multi-level User Interest and Multi-intent Fusion for Next Basket Recommendation by WEI Chuyuan, YUAN Baojie, WANG Changdong

“…A cross-level contrastive learning paradigm is also designed to combine item representations from different levels in order to enhance the semantic information between items at different levels. …”
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Intelligent measurement of adolescent idiopathic scoliosis x-ray coronal imaging parameters based on VB-Net neural network: a retrospective analysis of 2092 cases by Jinlong Liu, Haoran Zhang, Pei Dong, Danyang Su, Zhen Bai, Yuanbo Ma, Qiuju Miao, Shenyu Yang, Shuaikun Wang, Xiaopeng Yang

“…This study is based on the accurate and rapid measurement of x-ray coronal imaging parameters in AIS patients by AI, to explore the differences and correlations, and to further investigate the risk factors in different groups, so as to provide a theoretical basis for the diagnosis and surgical treatment of AIS. …”
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Satellite Image Time-Series Classification with Inception-Enhanced Temporal Attention Encoder by Zheng Zhang, Weixiong Zhang, Yu Meng, Zhitao Zhao, Ping Tang, Hongyi Li

“…Secondly, IncepTAE adopts one-branch architecture, which reinforces the interaction and congruity of different temporal information. Thirdly, the proposed IncepTAE is more lightweight due to the use of group convolutions. …”
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GANFlow: A Hybrid Model for SAR Image Target Open-Set Recognition Based on GAN and the Flow-Based Module by Jikai Qin, Jiusheng Han, Zheng Liu, Lei Ran, Rong Xie, Tat-Soon Yeo

“…In this model, a classifiable convolution GAN is first designed to complete the training of the feature extraction module and classifier. …”
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Single-Pixel Imaging Based on Enhanced Multi-Network Prior by Jia Feng, Qianxi Li, Jiawei Dong, Qing Zhao, Hao Wang

“…The SAE makes use of the measurement dimension information and uses the group inverse to obtain the decoding matrix to enhance its generalization. The Unet uses different size convolution kernels and attention mechanisms to enhance feature extraction capabilities. …”
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Total lesion glycolysis of primary tumor and lymphnodes is a strong predictor for development of distant metastases in oropharyngeal carcinoma patients with independent validation... by Sebastian Zschaeck, Marina Hajiyianni, Patrick Hausmann, Pavel Nikulin, Emily Kukuk, Christian Furth, Paulina Cegla, Elia Lombardo, Joanna Kazmierska, Adrien Holzgreve, Iosif Strouthos, Carmen Stromberger, Claus Belka, Michael Baumann, Mechthild Krause, Guillaume Landry, Witold Cholewinski, Jorg Kotzerke, Daniel Zips, Jörg van den Hoff, Frank Hofheinz

“…Additional analyses with manually delineated contours of the validation cohort revealed a similar effect (HR = 3.47, p = 0.026). No considerable differences between HPV positive and negative disease were observed. …”
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Analysis of preprocessing for Generative Adversarial Networks: A case study on color fundoscopy to fluorescein angiography image-to-image translation by Veena K.M., Veena Mayya, Rashmi Naveen Raj, Sulatha V. Bhandary, Uma Kulkarni

“…This study examines the impact of five different image preprocessing techniques - Green Channel, CLAHE on Green Channel, CLAHE on RGB channels, Green Channel Gaussian Convolution, and RGB Gaussian Convolution - on five different GAN variants: CycleGAN, Pix2Pix GAN, CUT GAN, FastCut GAN, and NICE GAN. …”
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Robustness of atmospheric trace gas retrievals obtained from low-spectral-resolution Fourier transform infrared absorption spectra under variations of interferogram length by B. Langerock, M. De Mazière, F. Desmet, P. Heikkinen, R. Kivi, M. Kumar Sha, C. Vigouroux, M. Zhou, G. K. Darbha, M. Talib

“…Shortening an interferogram can be part of standard FTIR data processing and typically occurs with a convolution operation on the interferogram. Shortening will alter the leakage pattern in the associated spectrum, and we demonstrate that the removal of a relatively small number of points from the interferogram edges creates a beat pattern in the difference of the associated spectra obtained from the original and shortened interferograms. …”
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Electrowetting display of multiscale Gamma based on dynamic histogram equilibrium by Mingzhen Chen, Zhixian Lin, Shanling Lin, Jianpu Lin, Tailiang Guo

“…Then, corresponding compensation weights are designed based on the different reflection brightness. Furthermore, the illumination component is extracted by multi-scale Gaussian convolution, and multi-scale gamma correction based on different photoelectric characteristics is designed. …”
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Liver segmentation network based on detail enhancement and multi-scale feature fusion by Lu Tinglan, Qin Jun, Qin Guihe, Shi Weili, Zhang Wentao

“…Additionally, 2D CT images obtained from different angles (such as sagittal, coronal, and transverse planes) increase the diversity of liver morphology and the complexity of segmentation. …”
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EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception by Yangyang Jiao, Yu Zhang, Yinke Dou, Liangliang Zhao, Qiang Liu

“…Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. …”
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Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model by Zilin Zhang, Yaohua Deng, Xiali Liu, Jige Liao

“…The experimental results on three different datasets indicate that the proposed model achieved high accuracy in fault diagnosis with relatively short data windows. …”
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Performance and Computational Efficiency of LMS Adaptive Volterra Equalizers for Nonlinear TWTA Distortion in Satellite Communications by Jerome J. Malone, Byeong Kil Lee

“…Optimum memory for equalization of QPSK, 8-PSK, and 16-QAM was found to be four linear and four or five cubic memory units. Difference in performance between fully-coupled and partially-decoupled adaptive equalizers was found to be small, rarely exceeding ~1.5 dB mean-square error. …”
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ShipYOLO: An Enhanced Model for Ship Detection by Xu Han, Lining Zhao, Yue Ning, Jingfeng Hu

“…In the training process, the 3 × 3 convolution, 1 × 1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. …”
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Spectrogram Features-Based Automatic Speaker Identification For Smart Services by Rashid Jahangir, Mohammed Alreshoodi, Fawaz Khaled Alarfaj

“…Traditionally, CNN employs square-shaped kernel and max-pooling operations at different layers, a design optimized to handle 2D data. …”
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Adaptive pixel attention network for hyperspectral image classification by Yuefeng Zhao, Chengmin Zai, Nannan Hu, Lu Shi, Xue Zhou, Jingqi Sun

“…More importantly, we also propose a new Adaptive Pixel Attention mechanism, which explores Cosine and Euclidean similarity to adaptively explore the distance and angle relationship between pixels of different scale convolution patch features. Moreover, the Cross-Layer Information Complement module is designed to form a contextual interaction by integrating the output features of different convolution layers, which can prevent the omission of discriminative information and further improve the network performance. …”
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DMSF-YOLO: Cow Behavior Recognition Algorithm Based on Dynamic Mechanism and Multi-Scale Feature Fusion by Changfeng Wu, Jiandong Fang, Xiuling Wang, Yudong Zhao

“…For the problem in multi-scale behavior changes of dairy cows, a multi-scale convolution module (MSFConv) is designed, and some C3k2 modules of the backbone network and neck network are replaced with MSFConv, which can extract cow behavior information of different scales and perform multi-scale feature fusion. …”
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