A robust NIfTI image authentication framework to ensure reliable and safe diagnosis

Basheer, Shakila; Singh, Kamred Udham; Sharma, Vandana; Bhatia, Surbhi; Pande, Nilesh Kumar U.; Kumar, Ankit

doi:10.7717/peerj-cs.1323

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…La práctica de transmitir imágenes radiológicas para interpretación y consulta ofrece numerosas ventajas como la recopilación rápida de datos, rentabilidad y mejor acceso a la atención sanitaria (Chirra et al, 2019;Basheer et al, 2023;Mackwood et al, 2022). Sin embargo, es importante considerar las desventajas asociadas con la teleradiología.…”

Section: Limitaciones Y Ventanas De Oportunidadunclassified

Medicina en la era digital: alcances y perspectivas

2024

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El libro Medicina en la era digital: alcances y perspectivas fue realizado gracias al auspicio del Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (papiit) de la Universidad Nacional Autónoma de México, con el proyecto TA400322 titulado “Medicina en la era digital”. Su objetivo es reconocer los avances que se han realizado en materia de medicina digital en nuestro país, así como los retos y dilemas que el uso de la tecnología plantea para los profesionales de la salud, los pacientes, tomadores de decisiones y la sociedad en general. Dividido en dos grandes secciones, la primera busca reconocer los antecedentes históricos, paradigmas, evolución y legislación en nuestro país de la salud digital. La segunda sección del libro busa analizar las problemáticas propias del uso de la medicina digital en distintos escenarios clínicos. Esta obra en su conjunto ofrece un panorama nacional de la implementación, adopción y legislación de la medicina digital en nuestro país planteando las bases históricas, epistemológicas y juridicas, así como los dilemas a los que estas nuevas tecnologías se enfrentan en distintos escenarios de la práctica médica. Esperamos que los análisis y discusiones expresadas generen interés en las y los lectores abonando a la argumentación plural y crítica de un tema relevante para la sociedad actual mexicana.

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Section: Limitaciones Y Ventanas De Oportunidadunclassified

Medicina en la era digital: alcances y perspectivas

2024

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“…The Neuroimaging Informatics Technology Initiative (NIfTI) is a standardized file format that stores and manages medical image data such as CT scans and MRI [ 23 ]. NIfTI was adopted as the default format to ensure compatibility and interoperability across different software tools and platforms [ 24 ] like 3D Slicer [ 25 ] and the Monai framework [ 26 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML)

Alsaleh,

Albalawi,

Algosaibi

et al. 2024

Diagnostics

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Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is often limited. To address this challenge, few-shot learning techniques have been successfully adapted to rapidly generalize to new tasks with only a few samples, leveraging prior knowledge. In this paper, we employ a gradient-based method known as Model-Agnostic Meta-Learning (MAML) for medical image segmentation. MAML is a meta-learning algorithm that quickly adapts to new tasks by updating a model’s parameters based on a limited set of training samples. Additionally, we use an enhanced 3D U-Net as the foundational network for our models. The enhanced 3D U-Net is a convolutional neural network specifically designed for medical image segmentation. We evaluate our approach on the TotalSegmentator dataset, considering a few annotated images for four tasks: liver, spleen, right kidney, and left kidney. The results demonstrate that our approach facilitates rapid adaptation to new tasks using only a few annotated images. In 10-shot settings, our approach achieved mean dice coefficients of 93.70%, 85.98%, 81.20%, and 89.58% for liver, spleen, right kidney, and left kidney segmentation, respectively. In five-shot sittings, the approach attained mean Dice coefficients of 90.27%, 83.89%, 77.53%, and 87.01% for liver, spleen, right kidney, and left kidney segmentation, respectively. Finally, we assess the effectiveness of our proposed approach on a dataset collected from a local hospital. Employing five-shot sittings, we achieve mean Dice coefficients of 90.62%, 79.86%, 79.87%, and 78.21% for liver, spleen, right kidney, and left kidney segmentation, respectively.

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“…The result of the semiautomatic segmentation is user-dependent. Similar work on identification of images is referred in [23], [24]. Another method of segmentation is based on clustering, in this clustering is done based on pixels.…”

Section: Tools and Librariesmentioning

confidence: 99%

Local post-hoc interpretable machine learning model for prediction of dementia in young adults

Sharma,

Midhunchakkaravarthy

2023

IJEECS

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<p>Dementia is still the prevailing brain disease with late diagnosis. There is a large increase in dementia disease among young adults. The major reason is over indulgence of young adults on social media resulting in denial of disease and delayed clinical diagnosis. Dementia is preventable and curable if diagnosed at an early stage, however, no attempts are being made to miti gate dementia in young adults. Today artificial intelligence (AI) based advanced technology with real-life consultations in clinical or remote setups are proved beneficial and is used to detect dementia. Most AI-based test is dependent on computer-aided di agnosis (CAD) tools and uses non-invasive imaging technology such as magnetic resonance imaging (MRI) data for disease diagnosis. In this paper, a local post-hoc interpretable machine learning (LPIML) model for prediction of dementia in young adults is proposed. The performance parameters are computed and compared based on accuracy, specificity, precision, F1 score and recall. The proposed work yields 98.87% training accuracy on original images and 99.31% training accuracy on morphologically enhanced images. The performance results are intrinsic and intuitive in learning the prediction results of individual case. The adoption of the proposed work will accelerate the diagnosis process in the era of digital healthcare.</p>

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A robust NIfTI image authentication framework to ensure reliable and safe diagnosis

Cited by 6 publications

References 27 publications

Medicina en la era digital: alcances y perspectivas

Medicina en la era digital: alcances y perspectivas

Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML)

Local post-hoc interpretable machine learning model for prediction of dementia in young adults

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