A surgical graphic console is presented. It allows the display of multimodal images (CT, MR and digital angiography), and the identification of tridimensional outlines of structures of surgical relevance, within a surgical reference system, together with the trajectory of surgical approach.
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Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system.
Sci Rep
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Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, Tamil Nadu, India.
In response to the pressing need for the detection of Monkeypox caused by the Monkeypox virus (MPXV), this study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN), a Capsule Network architecture designed for the precise multi-class classification of dermatological images. Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. CapsNets' inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes.
View Article and Find Full Text PDFMultilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions.
Sci Rep
January 2025
Shandong Provincial Public Health Clinical Center, Shandong University, Jinan, 250013, Shandong, China.
Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. However, its research on lesion segmentation for lung diseases is still limited, especially for pulmonary aspergillosis.
View Article and Find Full Text PDF[Investigation of the Influence of Image Reconstruction Parameters to Improve the Ability to Depict Internal Tumor Necrosis].
Nihon Hoshasen Gijutsu Gakkai Zasshi
January 2025
Department of Radiology, Nara Prefecture General Medical Center.
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View Article and Find Full Text PDFCausal Inference for Hypertension Prediction With Wearable E lectrocardiogram and P hotoplethysmogram Signals: Feasibility Study.
JMIR Cardio
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School of Life Science and Technology, University of Electronic Science and Technology of China, Research Building C348A, 3rd Fl, Chengdu, 611731, China, 86 18030493605.
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JMIR Form Res
January 2025
Department of Medical and Clinical Psychology, Center of Research on Psychological Disorders and Somatic Diseases (CoRPS), Tilburg University, Tilburg, the Netherlands, 31 134662142.
Background: Health-related data from technological devices are increasingly obtained through smartphone apps and wearable devices. These data could enable physicians and other care providers to monitor patients outside the clinic or assist individuals in improving lifestyle factors. However, the use of health technology data might be hampered by the reluctance of patients to share personal health technology data because of the privacy sensitivity of this information.
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