Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach.
Sci Rep
January 2025
Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.
View Article and Find Full Text PDFAn automatic cervical cell classification model based on improved DenseNet121.
Sci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
View Article and Find Full Text PDFAssessment of deep learning technique for fully automated mandibular segmentation.
Am J Orthod Dentofacial Orthop
February 2025
Department of Orthodontics, Faculty of Dentistry, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.
Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model.
Background: Placement of right precordial leads in higher intercostal spaces (EEP-ECG) improves the detection of Brugada Syndrome (BrS). Given the potential difficulty of lead placement and the transient nature of BrS ECG patterns, we developed a model to predict EEP-ECG from a standard 12‑lead ECG.
Objective: To create and validate a model that derives EEP-ECG leads from a standard 12‑lead ECG.
SEPO-FI: Deep-learning based software to calculate fusion index of muscle cells.
Comput Biol Med
January 2025
School of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea. Electronic address:
The fusion index is a critical metric for quantitatively assessing the transformation of in vitro muscle cells into myotubes in the biological and medical fields. Traditional methods for calculating this index manually involve the labor-intensive counting of numerous muscle cell nuclei in images, which necessitates determining whether each nucleus is located inside or outside the myotubes, leading to significant inter-observer variation. To address these challenges, this study proposes a three-stage process that integrates the strengths of pattern recognition and deep-learning to automatically calculate the fusion index.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!