Results 31 to 40 of about 10,752 (167)
Deep learning classification of chest x-ray images [PDF]
2020 IEEE SSIAI, Albuquerque, NM, USA, 2020, pp. 116-119, 2020 We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases.
arxiv +1 more source
Journal of Functional Morphology and Kinesiology, 2022 This study analyzed publicly available autopsy reports of male bodybuilders under the age of 50 who reportedly died from cardiovascular-related events. A general Google search with the terms “dead bodybuilders” was performed on 10 February 2022.
Guillermo Escalante +4 more
doaj +1 more source
Arquivos Brasileiros de Cardiologia, 2009 FUNDAMENTO: Pacientes com tetralogia de Fallot freqüentemente cursam com disfunção ventricular no período pós-operatório. A base histológica dessa alteração funcional tem sido pouco estudada.
Maria Cecília Knoll Farah +5 more
doaj +1 more source
Arquivos Brasileiros de Cardiologia, 2010 A insuficiência cardíaca com fração de ejeção normal (ICFEN) é uma síndrome complexa que vem sendo largamente estudada, desde a última década. É causada por disfunção ventricular diastólica evidenciada por métodos complementares, como estudo hemodinâmico
Meliza Goi Roscani +2 more
doaj +1 more source
Diffusion Probabilistic Models beat GANs on Medical Images [PDF]
Sci Rep 13, 12098 (2023), 2022 The success of Deep Learning applications critically depends on the quality and scale of the underlying training data. Generative adversarial networks (GANs) can generate arbitrary large datasets, but diversity and fidelity are limited, which has recently been addressed by denoising diffusion probabilistic models (DDPMs) whose superiority has been ...
arxiv +1 more source
Cardiomegaly Detection on Chest Radiographs: Segmentation Versus Classification
IEEE Access, 2020 In this study, we investigate the detection of cardiomegaly on frontal chest radiographs through two alternative deep-learning approaches - via anatomical segmentation and via image-level classification.
Ecem Sogancioglu +5 more
doaj +1 more source
Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture
Jurnal Sisfokom, 2023 Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual.
Tommy Saputra +3 more
doaj +1 more source
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models [PDF]
Sci Rep 13, 22576 (2023), 2023 Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets, preferably from multiple institutions. In federated learning (FL), a model is trained collaboratively at numerous sites that hold local datasets without exchanging them.
arxiv +1 more source
Cardiomegaly Detection using Deep Convolutional Neural Network with U-Net [PDF]
arXiv, 2022 Cardiomegaly is indeed a medical disease in which the heart is enlarged. Cardiomegaly is better to handle if caught early, so early detection is critical. The chest X-ray, being one of the most often used radiography examinations, has been used to detect and visualize abnormalities of human organs for decades.
arxiv
Generalized Lymphangiomatosis Presenting as Cardiomegaly
Journal of the Formosan Medical Association, 2007 Lymphangioma refers to the local proliferation of well-differentiated lymphatic tissue. Generalized lymphangiomatosis is rare. We report a previously healthy 8-month-old infant who suffered from tachypnea with mild fever for 2 weeks.
Yi-Ling Chen +4 more
doaj +1 more source