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| http://dx.doi.org/10.1002/cfg.241 | DOI Listing |
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Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach.
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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.
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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 PDFAutomated scoring to assess RAD51-mediated homologous recombination in ovarian patient-derived tumor organoids.
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Université de Caen Normandie, INSERM U1086 ANTICIPE, Caen, France; UNICANCER, Comprehensive Cancer Center François Baclesse, Caen, France; Université de Caen Normandie, US PLATON- ORGAPRED core facility, Caen, France; Université de Caen Normandie, US PLATON, UNICANCER, Comprehensive Cancer Center François Baclesse- Biological Resource Center 'OvaRessources', Caen, France. Electronic address:
PARP inhibitors (PARPi) have been shown to improve progression-free survival, particularly in homologous recombination deficient (HRD) ovarian cancers. Identifying patients eligible to PARPi is currently based on next-generation sequencing (NGS), but the persistence of genomic scars in tumors after restoration of HR or epigenetic changes can be a limitation. Functional assays could thus be used to improve this profiling and faithfully identify HRD tumors.
View Article and Find Full Text PDFSEPO-FI: Deep-learning based software to calculate fusion index of muscle cells.
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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 PDFImproving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection.
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Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.
To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level.
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