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  • 1.
    He, Lang
    et al.
    Xi'an University of Posts and Telecommunications, Xi'an, China.
    Li, Zheng
    Xi'an University of Technology, Xi'an, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Cao, Cui
    Weinan Normal University, Weinan, China.
    Xue, Jize
    Xi'an University of Posts and Telecommunications, Xi'an, China.
    Zhu, Feng
    The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
    Wu, Di
    First Affiliated Hospital, Air Force Medical University, Xi’an, Shaanxi, China.
    Depressformer: Leveraging Video Swin Transformer and fine-grained local features for depression scale estimation2024In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 96, no Part A, article id 106490Article in journal (Refereed)
    Abstract [en]

    Background and Objective:: By 2030, depression is projected to become the predominant mental disorder. With the rising prominence of depression, a great number of affective computing studies has been observed, with the majority emphasizing the use of audiovisual methods for estimating depression scales. Present studies often overlook the potential patterns of sequential data and not adopt the fine-grained features of Transformer to model the behavior features for video-based depression recognition (VDR). Methods: To address above-mentioned gaps, we present an end-to-end sequential framework called Depressformer for VDR. This innovative structure is delineated into the three structures: the Video Swin Transformer (VST) for deep feature extraction, a module dedicated to depression-specific fine-grained local feature extraction (DFLFE), and the depression channel attention fusion (DCAF) module to fuse the latent local and global features. By utilizing the VST as a backbone network, it is possible to discern pivotal features more effectively. The DFLFE enriches this process by focusing on the nuanced local features indicative of depression. To enhance the modeling of combined features pertinent to VDR, DCAF module is also presented. Results: Our methodology underwent extensive validations using the AVEC2013/2014 depression databases. The empirical results underscore its efficacy, yielding a root mean square error (RMSE) of 7.47 and a mean absolute error (MAE) of 5.49 for the first dataset. For the second database, the corresponding values were 7.22 and 5.56, respectively. And the F1-score is 0.59 on the D-vlog dataset. Conclusions: In summary, the experimental evaluations suggest that Depressformer architecture demonstrates superior performances with stability and adaptability across various tasks, making it capable of effectively identifying the severity of depression. Code will released at the link: https://github.com/helang818/Depressformer/. © 2024 Elsevier Ltd

  • 2.
    Khan, Hameed Ullah
    et al.
    Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
    Raza, Basit
    Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
    Shah, Munawar Hussain
    Pathology Department, Nishtar Medical University, Multan, Pakistan.
    Usama, Syed Muhammad
    Post Graduate Resident Surgeon at College of Physicians and Surgeons Pakistan (CPSP), Karachi, Pakistan.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Band, Shahab S.
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Yunlin, Douliou, Taiwan.
    SMDetector: Small mitotic detector in histopathology images using faster R-CNN with dilated convolutions in backbone model2023In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 81, article id 104414Article in journal (Refereed)
    Abstract [en]

    Breast cancer is one of the most common cancer types among women, and it is a deadly disease caused by the uncontrolled proliferation of cells. Pathologists face a challenging issue of mitotic cell identification and counting during manual detection and identification of cancer. Artificial intelligence can help the medical professional with early, quick, and accurate diagnosis of breast cancer. Consequently, the survival rate will be improved and mortality rate will be decreased. Different deep learning techniques are used in computational pathology for cancer diagnosis. In this study, the SMDetector is proposed which is a deep learning model for detecting small objects such as mitotic and non-mitotic nuclei. This model employs dilated layers in the backbone to prevent small objects from disappearing in the deep layers. The purpose of the dilated layers in this model is to reduce the size gap between the image and the objects it contains. Region proposal network is optimized to accurately identify small objects. The proposed model yielded overall average precision (AP) of 50.31% and average recall (AR) of 55.90% that outperforms the existing standard object detection models on ICPR 2014 (Mitos-Atypia-14) dataset. To best of our knowledge the proposed model is state-of-the-art model for precision and recall of mitotic object detection on ICPR 2014 (Mitos-Atypia-14) dataset. The proposed model has achieved average precision for mitotic nuclei 68.49%, average recall for mitotic nuclei 59.86% and f-measure 63.88%. © 2022 The Authors

  • 3.
    Ražanskas, Petras
    et al.
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Department of Electrical Power Systems, Kaunas University of Technology, Lithuania.
    Viberg, Per-Arne
    Swedish Adrenaline, Halmstad, Sweden.
    Olsson, Charlotte M.
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing2017In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, p. 19-29Article in journal (Refereed)
    Abstract [en]

    This article is concerned with a novel technique for prediction of blood lactate concentration level and oxygen uptake rate from multi-channel surface electromyography (sEMG) signals. The approach is built on predictive models exploiting a set of novel time-domain variables computed from sEMG signals. Signals from three muscles of each leg, namely, vastus lateralis, rectus femoris, and semitendinosus were used in this study. The feature set includes parameters reflecting asymmetry between legs, phase shifts between activation of different muscles, active time percentages, and sEMG amplitude. Prediction ability of both linear and non-linear (random forests-based) models was explored. The random forests models showed very good prediction accuracy and attained the coefficient of determination R2 = 0.962 for lactate concentration level and R2 = 0.980 for oxygen uptake rate. The linear models showed lower prediction accuracy. Comparable results were obtained also when sEMG amplitude data were removed from the training sets. A feature elimination algorithm allowed to build accurate random forests (R2 > 0.9) using just six (lactate concentration level) or four (oxygen uptake rate) time-domain variables. Models created to predict blood lactate concentration rate relied on variables reflecting interaction between front and back leg muscles, while parameters computed from front muscles and interactions between two legs were the most important variables for models created to predict oxygen uptake rate.© 2017 Elsevier Ltd.

  • 4.
    Viteckova, Slavka
    et al.
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Khandelwal, Siddhartha
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Kutilek, Patrik
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Krupicka, Radim
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Szabo, Zoltan
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Gait symmetry methods: Comparison of waveform-based Methods and recommendation for use2020In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 55, article id 101643Article in journal (Refereed)
    Abstract [en]

    Gait symmetry has been shown to be a relevant measure for differentiating between normal and pathological gait. Although a number of symmetry methods exist, it is not clear which of these methods should be used as they have been developed using data collected from varying experimental protocols. This paper presents a comparison of state-of-the-art waveform-based symmetry methods and tests them on walking data collected from different environments. Acceleration signals collected from the ankle are used to analyse symmetry methods under different signal circumstances, such as phase shift, waveform shape difference, signal length (i.e. number of gait cycles) and gait initiation phase. The cyclogram based method is invariant to signal phase shifts, signal length and the gait initiation phase. The trend symmetry method is not affected by signal scaling and the gait initiation phase but is affected by signal length depending on the environment. Similar to the trend method, the cross-correlation symmetry method is not responsive to signal scaling and the gait initiation phase. The results of the symbolic method are not influenced by signal scaling, gait initiation and depending on the environment by the signal phase shift. From the results of the performed analysis, we recommend the trend method to gait symmetry assessment. The comparison of waveform-based symmetry methods brings new knowledge that will help in selecting an appropriate method for gait symmetry assessment under different experimental protocols. © 2019 Elsevier Ltd. All rights reserved.

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