Risk assessment of thyroid nodules with a multi-instance convolutional neural network by Da Yu, Tingting Song, Yancheng Yu, Hebin Zhang, Feng Gao, Zirong Wang, Jiacheng Wang

“…Statistical analysis showed that the performance differences were statistically significant (p <0.0001).ConclusionsThese results demonstrate the effectiveness and clinical utility of the proposed MIL-CNN framework in non-invasively stratifying thyroid nodule risk, supporting more informed clinical decisions and potentially reducing unnecessary biopsies and surgeries. …”
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Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention by Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

“…Firstly, the trajectory slice difference encoder (TSDE) utilizes slice embedding (SE) to enrich the cross dimensional dependencies contained in the input sequence, and then combines Slice-Diff self attention (SDSA) and fine-grained convolution (FGC) to comprehensively capture sequence-specific positional and directional information. …”
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Scaling convolutional neural networks achieves expert level seizure detection in neonatal EEG by Robert Hogan, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin, Geraldine B. Boylan, John M. O’Toole

“…This model also attained expert-level performance on both validation sets, a first in this field, with no significant difference in inter-rater agreement when the model replaces an expert (∣Δ κ∣ < 0.094, p > 0.05).…”
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Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion by SUN Yuanshuai, KONG Fanqin, NIE Xiaoyin, XIE Gang

“…To address this problem, a cross-device fault diagnosis method based on graph convolution and multi-sensor fusion, convolutional domain graph convolution network (CDGCN) , was proposed. …”
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Brain-guided convolutional neural networks reveal task-specific representations in scene processing by Bruce C. Hansen, Michelle R. Greene, Henry A. S. Lewinsohn, Audrey E. Kris, Sophie Smyth, Binghui Tang

“…Here, we developed a novel brain-guided convolutional neural network (CNN) where each convolutional layer was separately guided by neural responses taken at different time points while observers performed a pre-cued object detection task or a scene affordance task on the same set of images. …”
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Ground-Based Remote Sensing Cloud Image Segmentation Using Convolution-MLP Network by Shuang Liu, Jiafeng Zhang, Zhong Zhang, Shuzhen Hu, Baihua Xiao

“…To this end, we propose the attention-guided MLPs module to highlight salient features and suppress irrelevant features from the spatial and channel aspects. Meanwhile, different from existing MLPs methods where the long-range dependencies are learned from one single scale, we propose the dilated MLPs (DMLPs) to learn long-range dependencies at different scales by sampling different channels of tokens. …”
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Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine by Mohammad Hoseintabar Marzebali, Saeed Hasani Borzadaran, Hoda Mashayekhi, Valiollah Mashayekhi

“…Therefore, a proper condition monitoring method that can classify the type and the severity of electrical machine faults in different load levels is crucial to avoid unwanted downtime and loss of operation. …”
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Comparative exploration of deep convolutional neural networks using real-time endoscopy images by Subhashree Mohapatra, Pukhraj Singh Jeji, Girish Kumar Pati, Manohar Mishra, Tripti Swarnkar

“…Until now various deep convolutional neural networks are designed and trained for the purpose of classifying different medical conditions related to the domain of gastroenterology. …”
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The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images by Pei Zhang, Qijian Xu, Feng Jiang

“…ObjectiveTo extract and analyze the image features of two-dimensional ultrasound images and elastic images of four thyroid nodules by radiomics, and then further convolution processing to construct a prediction model for thyroid cancer. …”
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Fast and intelligent detection of concrete cracks based on sound signals and convolutional neural network by Chunlei Ge, Yue Qin, Yue Qin, Kaizhong Xie, Zubiao Lu

“…Finally, comparative experiments with different frame lengths, different models and different signal-to-noise ratios (SNR) are conducted using the improved CNN.ResultsThe results show that the model validation process has the least loss and highest accuracy when the input frame length is 1024. …”
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A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data by Kinh Bac Dang, Van Bao Dang, Quang Thanh Bui, Van Vuong Nguyen, Thi Phuong Nga Pham, Van Liem Ngo

“…Nowadays, the integration of deep learning in remote sensing and GIS analysis can quickly classify and detect different characteristics on both land and sea. Therefore, the authors proposed the use of a convolutional neural network (ConvNet) for coastal classification based on these technologies and geomorphic profile graphs. …”
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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network by Jiahui E, Lu Wang, Chunhui Zhao, P. Takis Mathiopoulos, Tomoaki Ohtsuki, Fumiyuki Adachi

“…To deal with these problems this article proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network. First, employing Multiscale Superpixel Reconstructed Difference Image effectively enhances the edge information of the images. …”
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Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition by Yuan Xie, Tao Zhang

“…We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.…”
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Chasing Dragons in the Dragon's Land: A Convoluted Struggle with Drugs and Deviance in Modern China by Aftab Haider, Ashraf Ali, Muhammad Zubair

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MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction by Yong Zhang, Yong Yin, Ning Xu, Bowen Jia

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Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks by Suzhao Bi, Rongjian Lu, Qiang Xu, Peiwen Zhang

“…To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN). The model integrates three convolutional branch layers with different kernel sizes and dilation rates to capture features across varying temporal scales. …”
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Method for predicting cutter remaining life based on multi-scale cyclic convolutional network by Tao Li, Yinquan Yu, Chaoqiang Huang, Jinwen Yang, Yuanchang Zhong, Yong Hao

“…In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life prediction. …”
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Land-sea Clutter Classification Method Based on Multi-channel Graph Convolutional Networks by Can LI, Zengfu WANG, Xiaoxuan ZHANG, Quan PAN

“…Based on radar parameters, data characteristics, and sample proportions, we construct a land-sea clutter original dataset containing 12 different scenes and a land-sea clutter scarce dataset containing 36 different configurations. …”
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Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks by Masoud Geravanchizadeh, Amir Shaygan Asl, Sebelan Danishvar

“…Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. …”
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Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks by José Miguel Valiente, Juan José Martín-Osuna, Ana María Peral, Isabel Escriche

“…This Ground Truth termed POLLEN24_SP, comprises 32,285 pollen/particle images (captured by an expert using optical microscopy), covering the 24 most prevalent types of pollen grains found in Spanish honeys. Twelve different pre-existing Convolutional Neural Networks (CNN) were evaluated, achieving an accuracy rate of up to 98.03 % with EfficientNetV2M. …”
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