The use of deep neural networks ("deep learning") creates new possibilities in digital image processing. This approach has been widely applied and successfully used for the evaluation of image data in ophthalmology. In this article, the methodological approach of deep learning is examined and compared to the classical approach for digital image processing. The differences between the approaches are discussed and the increasingly important role of training data for model generation is explained. Furthermore, the approach of transfer learning for deep learning is presented with a representative data set from the field of corneal confocal microscopy. In this context, the advantages of the method and the specific problems when dealing with medical microscope data will be discussed.
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http://dx.doi.org/10.1055/a-1008-9400 | DOI Listing |
Braz Oral Res
January 2025
Universidade Estadual de Campinas - Unicamp, School of Applied Sciences, Campinas, SP, Brazil.
Social networks consist of a group of individuals connected by family, work, or other interests and facilitated by an online structure or platform. They are also a relatively recent and widely used marketing phenomenon that is constantly evolving. The healthcare field includes professions such as social work, biology, biomedicine, physical education, nursing, pharmacy, physiotherapy, speech therapy, medicine, veterinary medicine, nutrition, dentistry, psychology, and occupational therapy.
View Article and Find Full Text PDFPLoS One
January 2025
Laboratory of Hepato-Gastroenterology, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium.
The emerging field of 3D organ modeling encounters several imaging issues in particular related to antigen retrieval and sample loss during staining processes. Due to their compact shape, several antibodies fail to penetrate intact organoids or spheroids. Histology of organoids can be approached by paraffin inclusion and sectioning at 5 μm as performed for biopsies.
View Article and Find Full Text PDFPLoS One
January 2025
Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America.
There is growing interest in using deep learning models to automate wildlife detection in aerial imaging surveys to increase efficiency, but human-generated annotations remain necessary for model training. However, even skilled observers may diverge in interpreting aerial imagery of complex environments, which may result in downstream instability of models. In this study, we present a framework for assessing annotation reliability by calculating agreement metrics for individual observers against an aggregated set of annotations generated by clustering multiple observers' observations and selecting the mode classification.
View Article and Find Full Text PDFNetwork
January 2025
Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM).
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montreal, QC, Canada.
Objectives: To compare thoracolumbar fascia (TLF) shear strain between individuals with and without nonspecific low back pain (NSLBP), investigate its correlation with symptoms, and assess a standardized massage technique's impact on TLF shear strain.
Methods: Participants were prospectively enrolled between February 2021 and June 2022. Pre- and post-intervention TLF ultrasound and pain/disability questionnaires were conducted.
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