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Wound Segmentation with U-Net Using a Dual Attention Mechanism and Transfer Learning.
J Imaging Inform Med
January 2025
Computer Science Department, University of Geneva, Geneva, Switzerland.
Accurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image analysis. In this paper, we introduce a novel dual attention U-Net model designed for precise wound segmentation. Our proposed architecture integrates two widely used deep learning models, VGG16 and U-Net, incorporating dual attention mechanisms to focus on relevant regions within the wound area.
View Article and Find Full Text PDFClassifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework.
Sci Rep
January 2025
Electrical and Computer Engineering Department, University of Memphis, Memphis, TN, 38152, USA.
Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC grading. However, current methods for TILs quantification are based on subjective visual assessments, leading to inter-observer variability and inconsistent diagnostic reproducibility.
View Article and Find Full Text PDFIdentification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach.
Sci Rep
January 2025
Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh.
Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising global health concerns due to its unprecedented spread in non-endemic regions. Recent studies have shown that Clade IIb (2022 MPXV) is marked by unique genomic mutations and epidemiological behaviors, suggesting variations in host-virus interactions. This study aimed to identify the differentially expressed genes (DEGs) induced by the 2022 MPXV infection through comprehensive bioinformatics analyses of microarray and RNA-Seq datasets from post-infected cell types with different MPXV clades.
View Article and Find Full Text PDFA novel domain feature disentanglement method for multi-target cross-domain mechanical fault diagnosis.
ISA Trans
January 2025
State Key Laboratory of Computer-Aided Design and Computer Graphics, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Intelligent Rescue Equipment for Collapse Accidents, Ministry of Emergency Management, Hangzhou, 310030, China; Zhejiang Laboratory, Hangzhou, 311121, China. Electronic address:
Existing cross-domain mechanical fault diagnosis methods primarily achieve feature alignment by directly optimizing interdomain and category distances. However, this approach can be computationally expensive in multi-target scenarios or fail due to conflicting objectives, leading to decreased diagnostic performance. To avoid these issues, this paper introduces a novel method called domain feature disentanglement.
View Article and Find Full Text PDFPhotoacoustic Imaging with Attention-Guided Deep Learning for Predicting Axillary Lymph Node Status in Breast Cancer.
Acad Radiol
January 2025
Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, Guangdong, China (G.L., S.T., Z.H., M.W., S.M., J.X., F.D.); Department of Ultrasound, The First Affiliated Hospital, Southern University of Science and Technology (Shenzhen People's Hospital), Shenzhen 518020, Guangdong, China (H.T., H.W., J.X., F.D.). Electronic address:
Rationale And Objectives: Preoperative assessment of axillary lymph node (ALN) status is essential for breast cancer management. This study explores the use of photoacoustic (PA) imaging combined with attention-guided deep learning (DL) for precise prediction of ALN status.
Materials And Methods: This retrospective study included patients with histologically confirmed early-stage breast cancer from 2022 to 2024, randomly divided (8:2) into training and test cohorts.
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