Medical Imaging
This dataset is collected from Kaggle ( https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset ). This dataset is a combination of the following three datasets :
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The demand for artificial intelligence (AI) in healthcare is rapidly increasing. However, significant challenges arise from data scarcity and privacy concerns, particularly in medical imaging. While existing generative models have achieved success in image synthesis and image-to-image translation tasks, there remains a gap in the generation of 3D semantic medical images. To address this gap, we introduce Med-DDPM, a diffusion model specifically designed for semantic 3D medical image synthesis, effectively tackling data scarcity and privacy issues.
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2538 Views
The morphological characteristics of skeletal muscles, such as fascicle orientation, fascicle length, and muscle thickness, contain valuable mechanical information that aids in understanding muscle contractility and excitation due to commands from the central nervous system. Ultrasound (US) imaging, a non-invasive measurement technique, has been employed in clinical research to provide visualized images that capture morphological characteristics. However, accurately and efficiently detecting the fascicle in US images is challenging.
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196 Views
Elastography is a non-invasive technique to detect tissue anomalies via the local elastic modulus using shear waves. Commonly shear waves are produced via acoustic focusing or the use of mechanical external sources, shear waves may result also naturally from cavitation bubbles during medical intervention, for example from thermal ablation. Here, we measure the shear wave emitted from a well-controlled single laser-induced cavitation bubble oscillating near a rigid boundary. The bubbles are generated in a transparent tissue-mimicking hydrogel embedded with tracer particles.
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11 Views
This simulation dataset contains five types of data: resolutions, vessels, vessel stenosis, tumors, and shape combinations. There are a total of 1000 original binary images. Besides, we set different gray values on images with multiple connected domains to simulate different concentration of magnetic nanoparticles. Next, the images are subjected to operations such as image inversion and image rotation. The final dataset contains 20,000 images. we applied the X-space method based on the X-space theory and we generated the simulated image of magnetic particle imaging.
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218 Views
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350 Views
This Dataset used a non-invasive blood group prediction approach using deep learning. Rapid and meticulous prediction of blood type is a major step during medical emergency before supervising the red blood cell, platelet, and plasma transfusion. Any small mistake during transfer of blood can cause death. In conventional pathological assessment, the blood test is conducted using automated blood analyser; however, it results into time taking process.
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2568 Views
Tuberculosis (TB) remains a major global health problem with high incidence and mortality rates worldwide. In recent years, with the rapid development of computer-aided diagnosis (CAD) tools, CAD has played an increasingly important role in supporting tuberculosis diagnosis. However, the development of CAD for TB diagnosis relies heavily on well-annotated computerized tomography (CT) datasets. Unfortunately, the currently available annotations in TB CT datasets are still limited, which hinders the development of CAD tools for TB diagnosis to some extent.
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9 Views
Tuberculosis (TB) remains a major global health problem with high incidence and mortality rates worldwide. In recent years, with the rapid development of computer-aided diagnosis (CAD) tools, CAD has played an increasingly important role in supporting tuberculosis diagnosis. However, the development of CAD for TB diagnosis relies heavily on well-annotated computerized tomography (CT) datasets. Unfortunately, the currently available annotations in TB CT datasets are still limited, which hinders the development of CAD tools for TB diagnosis to some extent.
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43 Views
Accurate detection and segmentation of brain tumors is critical for medical diagnosis. We propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor segmentation. TSGM was trained on the BraTs2020 brain tumor dataset.
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33 Views