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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 PDFEffect of Fabricating Process on the Performance of Two-Dimensional p-Type WSe Field Effect Transistors.
Nano Lett
January 2025
Hunan Provincial Key Laboratory of Two-Dimensional Materials, State Key Laboratory for Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Two-dimensional (2D) transition metal dichalcogenides (TMDs), such as WSe, are promising candidates for next-generation integrated circuits. However, the dependence of intrinsic properties of TMD devices on various processing steps remains largely unexplored. Here, using pristine p-type WSe devices as references, we comprehensively studied the influence of each step in traditional nanofabrication methods on device performance.
View Article and Find Full Text PDFMVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.
Int J Biol Macromol
January 2025
College of Information Science and Engineering, Northeastern University, China.
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental methods, accurate computational approaches for protein-protein interaction sites (PPIS) prediction can save significant time and costs.
View Article and Find Full Text PDFEmbracing the Future of Clinical Trials in Radiotherapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.
Int J Radiat Oncol Biol Phys
January 2025
National Cancer Institute, Bethesda, MD. Electronic address:
This white paper examines the potential of pioneering technologies and artificial intelligence (AI)-driven solutions in advancing clinical trials involving radiotherapy. As the field of radiotherapy evolves, the integration of cutting-edge approaches such as radiopharmaceutical dosimetry, FLASH radiotherapy, image-guided radiation therapy (IGRT), and AI promises to improve treatment planning, patient care, and outcomes. Additionally, recent advancements in quantum science, linear energy transfer/relative biological effect (LET/RBE), and the combination of radiotherapy and immunotherapy create new avenues for innovation in clinical trials.
View Article and Find Full Text PDFThe ReAct project: Analysis of data from 23 different laboratories to characterise DNA recovery given two sets of activity level propositions.
Forensic Sci Int Genet
January 2025
Bundeskriminalamt, Wiesbaden, Germany; International Commission on Missing Persons, The Hague, The Netherlands.
The ReAct (Recovery, Activity) project is an ENFSI (European Network of Forensic Science Institutes) supported initiative comprising a large consortium of laboratories. Here, the results from more than 23 laboratories are presented. The primary purpose was to design experiments simulating typical casework circumstances; collect data and to implement Bayesian networks to assess the value (i.
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