Article
Green & Sustainable Science & Technology
Fucong Liu, Hui Xu, Miao Qi, Di Liu, Jianzhong Wang, Jun Kong
Summary: In this paper, a Depth-wise Separable Convolution Attention Module (DSCAM) is proposed for garbage image classification, with a residual network as the backbone. Experimental results demonstrate the effectiveness of the proposed method, outperforming some classical methods.
Article
Computer Science, Artificial Intelligence
Wenzhao Cui, Qing Zhang, Baochuan Zuo
Summary: This paper proposes a novel saliency detection method based on deep learning, introducing the DCAM network structure that incorporates multi-scale features and enlarges the receptive field, using DAM to guide each side output to enhance the representation ability of each layer. Experimental results demonstrate that the method performs favorably on five benchmark datasets, with fast speed, outperforming five existing methods.
Article
Chemistry, Multidisciplinary
Lars Hempel, Sina Sadeghi, Toralf Kirsten
Summary: This paper presents predictive models for the length of stay (LOS) of ICU patients using machine learning and early available clinical data. The goal was to demonstrate a practical approach to predicting LOS and improve resource allocation and future planning. The results show significant improvements in the performance of the models for predicting actual LOS.
APPLIED SCIENCES-BASEL
(2023)
Article
Neurosciences
Wei Zhou, Jianhang Ji, Yan Jiang, Jing Wang, Qi Qi, Yugen Yi
Summary: In this paper, a one-stage network called EARDS is proposed for joint OD and OC segmentation. The network combines EfficientNet and attention-based residual depth-wise separable convolution to improve segmentation performance and computational efficiency. Experimental results show that this method outperforms existing approaches and provides accurate assistance for glaucoma diagnosis.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Engineering, Multidisciplinary
Merhan A. Abd-Elrazek, Ahmed A. Eltahawi, Mohamed H. Abd Elaziz, Mohamed N. Abd-Elwhab
Summary: According to the World Health Organization (WHO), patient Length of Stay (LOS) in hospitals is an important performance measurement and monitoring indicator. Prolonged LOS in the Intensive Care Unit (ICU) may lead to consuming hospital resources, manpower, and equipment. The proposed framework for predicting patient LOS in the ICU using different machine learning (ML) techniques demonstrates high prediction accuracy and applicability across all patients.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Biochemistry & Molecular Biology
Animesh Acharjee, Jon Hazeldine, Alina Bazarova, Lavanya Deenadayalu, Jinkang Zhang, Conor Bentley, Dominic Russ, Janet M. Lord, Georgios V. Gkoutos, Stephen P. Young, Mark A. Foster
Summary: Recent advances in emergency medicine and trauma care have increased the survival rates of critically-injured patients. However, this has also put a strain on the healthcare system. This study investigated the serum metabolomic profile of major trauma patients and found that combining metabolomic data with clinical scoring systems can accurately identify patients with extended ICU stays.
Article
Mathematics
Ke Zhang, Yaming Guo
Summary: Traffic accidents have a significant impact on public safety and economic development, making their prevention crucial in urban transportation. This paper introduces a deep learning method called ARDN, which effectively predicts accidents by extracting essential information from diverse datasets.
Article
Engineering, Biomedical
Vibha Bhandari, Narendra D. Londhe, Ghanahshyam B. Kshirsagar
Summary: The P300 speller is a challenging task, and we propose a novel solution to overcome the various factors that make it difficult. Our proposed model incorporates temporal dilated convolution and channel-wise attention, and employs cost-sensitive learning to handle data imbalance. Through extensive experiments, we demonstrate significant improvement in P300 classification with reduced trainable parameters compared to single-trial experiments. Our approach outperforms state-of-the-art compact models and offers a better balance between computational complexity and classification performance, providing new insights and inspiration for future research in the field of P300 classification.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Hemraj Singh, Mridula Verma, Ramalingaswamy Cheruku
Summary: In this article, the authors propose DSCNet and BSCNet models for video salient object detection tasks, achieving good performance. These models can effectively detect objects in unconstrained scenarios with multiple challenges and have fast training speed and good evaluation metrics.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Multidisciplinary
Chunli Lei, Jiashuo Shi, Shuzhen Ma, Linlin Xue, Mengxuan Jiao, Jianhua Li
Summary: This paper proposes a bearing fault diagnosis method based on mixed attention mechanism (MAM) and deep separable dilated convolution neural network (DSDCNN) to overcome the problems of traditional fault diagnosis methods. The method transforms the original one-dimensional vibration signal into a two-dimensional feature image with temporal correlation using the Markov transfer field encoding method. It then takes advantage of the low computational complexity of deep separable convolution and the ability of dilated convolution to expand the receptive field to improve the recognition accuracy.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Clinical Neurology
Guoxin Fan, Sheng Yang, Huaqing Liu, Ningze Xu, Yuyong Chen, Jie He, Xiuyun Su, Mao Pang, Bin Liu, Lanqing Han, Limin Rong
Summary: This study developed machine-learning classifiers to predict prolonged ICU-stay and prolonged hospital-stay for critical patients with spinal cord injury. The ensemble classifiers showed high potential in assisting physicians in managing SCI patients in ICU and optimizing the use of medical resources.
Article
Computer Science, Artificial Intelligence
Masum Shah Junayed, Md Baharul Islam, Hassan Imani, Tarkan Aydin
Summary: This paper proposes a lightweight model for object detection and classification, using point-wise separable and depth-wise separable convolutions for feature extraction and a spatial feature pyramid network for accurate detection and categorization. The experiments show that the proposed model outperforms existing backbones in three publicly accessible datasets.
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
(2022)
Article
Environmental Sciences
Rasha Alshawi, Md Tamjidul Hoque, Maik C. Flanagin
Summary: In this paper, an improved U-Net model is proposed, which achieves higher performance and reduced complexity by introducing a sparsely connected depth-wise separable block with multiscale filters. The use of depth-wise separable convolution significantly reduces trainable parameters, making the training faster and reducing the risk of overfitting. The proposed model outperforms state-of-the-art methods, achieving higher accuracy and IoU on the sinkhole and nuclei datasets.
Article
Computer Science, Artificial Intelligence
Masum Shah Junayed, Md Baharul Islam, Hassan Imani, Tarkan Aydin
Summary: This paper proposes a new lightweight model using point-wise separable and depth-wise separable convolutions for feature extraction, and a spatial feature pyramid network for accurate object detection and classification. The proposed model outperforms existing models in multiple datasets and shows high performance and real-time capability.
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
(2022)
Article
Geochemistry & Geophysics
Gaodian Zhou, Weitao Chen, Qianshan Gui, Xianju Li, Lizhe Wang
Summary: The article introduces a method to improve the accuracy of road extraction from high-resolution remote-sensing images using a split depth-wise separable graph convolutional network. The results of the experiment show that this method performs better in extracting covered and tiny roads.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)