The radiation detected by the nuclear probe may be reconstructed into an image based on the detected transducer position since the nuclear probe moves with the ultrasound transducer. Both anatomical and functional imaging may be provided together without the complications of calibration and tracking. ...
[深度学习从入门到女装]Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning,程序员大本营,技术文章内容聚合第一站。
K. Charles, and E. Munson, "Multi-modal medical image retrieval," SPIE Medical Imaging, 2011.Kalpathy-Cramer, J., Hersh, W.: Multimodal medical image retrieval: image categorization to improve search precision. In: Proceedings of the international conference on Multimedia information retrieval, ...
Papp, L.; Zsoter, N.; Szabo, G.; Bejan, C.; Szimjanovszki, E.; Zuhayra, M. 2009:Parallel registration of multi-modal medical image triples having unknown inter-image geometryAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicin...
www.nature.com/scientificreports OPEN Multi‑modal transformer architecture for medical image analysis and automated report generation Santhosh Raminedi 1,4, S. Shridevi 2* & Daehan Won 3,4 Medical practitioners examine medical images, such as X-rays, write reports ...
In recent years, deep learning models comprising transformer components have pushed the performance envelope in medical image synthesis tasks. Contrary to convolutional neural networks (CNNs) that use static, local filters, transformers use self-attention mechanisms to permit adaptive, non-local filtering...
To utilize context correlation between coefficients in contourlet domain, a novel multi-modal medical image fusion method based on contextual information is proposed. First, the context information of contourlet coefficients are calculated to capture the strong dependencies of coefficients. Second, hidden ...
Automated retinal image medical description generation is crucial for streamlining medical diagnosis and treatment planning. Existing challenges include the reliance on learned retinal image representations, difficulties in handling multiple imaging modalities, and the lack of clinical context in visual represent...
In recent years, deep learning models comprising transformer components have pushed the performance envelope in medical image synthesis tasks. Contrary to convolutional neural networks (CNNs) that use static, local filters, transformers use self-attention mechanisms to permit adaptive, non-local filtering...
The number of datasets and computational efficiency are always hindrances in the multi-modal medical image fusion (MMIF) research. To address these challenges, we propose a contrastive learning framework inspired meta-mutual, which divides the medical image fusion task into subtasks and pre-trains an...