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VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).
Microsc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
View Article and Find Full Text PDFIntegration of unpaired single cell omics data by deep transfer graph convolutional network.
PLoS Comput Biol
January 2025
School of Mathematics/Harbin Institute of Technology, Harbin, China.
The rapid advance of large-scale atlas-level single cell RNA sequences and single-cell chromatin accessibility data provide extraordinary avenues to broad and deep insight into complex biological mechanism. Leveraging the datasets and transfering labels from scRNA-seq to scATAC-seq will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the lower capable of preserving fine-grained cell populations and intrinsic or extrinsic heterogeneity between datasets.
View Article and Find Full Text PDFExplainable analysis of infrared and visible light image fusion based on deep learning.
Sci Rep
January 2025
Shandong University of Science and Technology, Qingdao, 266590, China.
Explainability is a very active area of research in machine learning and image processing. This paper aims to investigate the explainability of visible light and infrared image fusion technology in order to enhance the credibility of model understanding and application. Firstly, a multimodal image fusion model was proposed based on the advantages of convolutional neural networks (CNN) for local context extraction and Transformer global attention mechanism.
View Article and Find Full Text PDFDeep Serratus Anterior Plane Block for Multimodal Analgesia in Minimally Invasive Mitral Valve Surgery Performed via Right Anterior Mini-Thoracotomy.
J Cardiothorac Vasc Anesth
December 2024
Department of Medical Science, Surgery and Neurosciences, Cardiothoracic and Vascular Anesthesia and Intensive Care Unit, University Hospital of Siena, Siena, Italy. Electronic address:
Objective: This study investigated if the serratus anterior plane block (SAPB) within a multimodal analgesia scheme would reduce acute post-operative pain and intravenous opioid consumption in patients admitted to the intensive care unit after isolated minimally invasive mitral valve surgery.
Design: Retrospective study.
Setting: Patients were admitted to the intensive care unit (ICU) of the University Hospital of Siena (Italy).
MulitDeepsurv: survival analysis of gastric cancer based on deep learning multimodal fusion models.
Biomed Opt Express
January 2025
Computer Engineering Department, Taiyuan Institute of Technology, Taiyuan 030008, China.
Gastric cancer is a leading cause of cancer-related deaths globally. As mortality rates continue to rise, predicting cancer survival using multimodal data-including histopathological images, genomic data, and clinical information-has become increasingly crucial. However, extracting effective predictive features from this complex data has posed challenges for survival analysis due to the high dimensionality and heterogeneity of histopathology images and genomic data.
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