Publications by authors named "Manali Saini"
Article Synopsis
- * This study introduces a new, efficient deep learning model called a dense multiplicative attention enhanced encoder-decoder network that improves segmentation without significantly increasing computational time.
- * Performance evaluations show strong results with dice coefficients of 0.83 and 0.86 on public datasets, and the network also proves effective in real-world scenarios with a dice coefficient of 0.72 despite noise, indicating its practicality for real-time application.
Article Synopsis
- Contrast-free ultrasound imaging can effectively assess both benign and malignant lesions, but motion from nearby structures, like the carotid artery, significantly impacts image quality in the thyroid region.* -
- This study presents a low-complex deep learning network that reduces inter-frame motion artifacts in ultrasound imaging, which enhances the visual quality of tumor microvessels.* -
- Validation results reveal substantial improvements in image correlation metrics, with the proposed method showing a 20% increase in performance for in-vivo data and achieving real-time motion correction with a dramatic reduction in processing time.*
This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT).
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