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Improved self-training-based distant label denoising method for cybersecurity entity extractions.
PLoS One
December 2024
School of Cyber Science and Engineering, Sichuan University, Chengdu, China.
The task of named entity recognition (NER) plays a crucial role in extracting cybersecurity-related information. Existing approaches for cybersecurity entity extraction predominantly rely on manual labelling data, resulting in labour-intensive processes due to the lack of a cybersecurity-specific corpus. In this paper, we propose an improved self-training-based distant label denoising method for cybersecurity entity extraction.
View Article and Find Full Text PDFSURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition.
Sensors (Basel)
November 2024
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
We propose a self-training scheme, SURABHI, that trains deep-learning keypoint detection models on machine-annotated instances, together with the methodology to generate those instances. SURABHI aims to improve the keypoint detection accuracy not by altering the structure of a deep-learning-based keypoint detector model but by generating highly effective training instances. The machine-annotated instances used in SURABHI are hard instances-instances that require a rectifier to correct the keypoints misplaced by the keypoint detection model.
View Article and Find Full Text PDFCalibrated Adaptive Teacher for Domain-Adaptive Intelligent Fault Diagnosis.
Sensors (Basel)
November 2024
Intelligent Maintenance and Operations Systems, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Intelligent fault diagnosis (IFD) based on deep learning can achieve high accuracy from raw condition monitoring signals. However, models usually perform well on the training distribution only, and experience severe performance drops when applied to a different distribution. This is also observed in fault diagnosis, where assets are often operated in working conditions different from the ones in which the labeled data have been collected.
View Article and Find Full Text PDFCACs Recognition of FISH Images Based on Adaptive Mean Teacher Semi-supervised Learning with Domain-Knowledge Pseudo Label.
J Imaging Inform Med
December 2024
Zhuhai Hengqin Sanmed Aitech Inc,, Zhuhai, Guangdong, China.
Circulating genetically abnormal cells (CACs) serve as crucial biomarkers for lung cancer diagnosis. Detecting CACs holds great value for early diagnosis and screening of lung cancer. To aid the identification of CACs, we have incorporated deep learning algorithms into our CACs detection system, specifically developing algorithms for cell segmentation and signal point detection.
View Article and Find Full Text PDFSelf-Supervised Super-Resolution of 2D Pre-clinical MRI Acquisitions.
Proc SPIE Int Soc Opt Eng
February 2024
Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
Animal models are pivotal in disease research and the advancement of therapeutic methods. The translation of results from these models to clinical applications is enhanced by employing technologies which are consistent for both humans and animals, like Magnetic Resonance Imaging (MRI), offering the advantage of longitudinal disease evaluation without compromising animal welfare. However, current animal MRI techniques predominantly employ 2D acquisitions due to constraints related to organ size, scan duration, image quality, and hardware limitations.
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