A total of 1035 color Doppler US images of heart patients, who were suffering from MR has been collected from the department of cardiology, Swami Rama Himalayan University (SHRU), Dehradun, India. The US images (800 × 600 pixels) used for the analysis of MR were recorded by Philips US machine equipped with multi-frequency transducers of 2-5 MHz range. The images were collected in three different views, i.e., A2C, A4C and PLAX view.
This paper introduces the Chinese Social Media Autism Children Dataset (CSMACD), a novel resource for autism spectrum disorder (ASD) research. CSMACD compiles high-definition, unobstructed frontal facial images of Chinese children (aged 6 months to 15 years) with ASD, sourced from mainstream social media platforms (e.g., Bilibili, Douyin, and Tencent Video). Videos were identified using ASD-related keywords (e.g., "autism," "Star Baby") and recommendation algorithms.
Autism Spectrum disorder brain sMRI image
The rapid advancement of generative neural networks has facilitated the creation of photorealistic images, raising concerns about the proliferation of misinformation. Detecting AI-generated fakes has become crucial, given their potential impact on public opinion and various sectors. This dataset presents a comparative analysis of real and AI-generated images, focusing on building a novel dataset named Realistic AI-Generated Image (RealAIGI) dataset.
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Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction.
Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.
Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.
Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.
Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.
Fair Use for Academic Research: If you use this dataset, please cite the following paper to ensure proper attribution
M. A. Onsu, P. Lohan, B. Kantarci, A. Syed, M. Andrews, S. Kennedy, "Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring," 30th IEEE Symposium on Computers and Communications (ISCC), July 2025, Bologna, Italy.
Preprint available here: https://arxiv.org/pdf/2502.11304
This dataset includes conjunctival and retinal images collected from both diabetic and healthy individuals to support research on diabetes-related vascular changes. For each subject, eight conjunctival images (four per eye: looking left, right, up, and down) are provided. Subjects with diabetes additionally have corresponding left and right retinal fundus images. Metadata for diabetic participants includes classification into subgroups: diabetes only, diabetes with retinopathy, or diabetes with related complications such as hypertension.
