Self-supervised Multi-modal Alignment for Whole Body Medical Imaging

Development of Convergence Nanoparticles for Multi-Modal Bio-Medical Imaging

Probabilistic Semi-Supervised Multi-Modal Hashing

In this paper, we propose a non-parametric Bayesian framework for multi-modal hash learning that takes into account the distance supervision (similarity/dissimilarity constraints). Our model embeds data of arbitrary modalities into a single latent binary feature with the ability to learn the dimensionality of the binary feature using the data itself. Given supervisory information (labeled simil...

Whole-Body Diffusion Imaging Applying Simultaneous Multi-Slice Excitation.

PURPOSE The purpose of this study was to examine the feasibility of a fast protocol for whole-body diffusion-weighted imaging (WB-DWI) using a slice-accelerated echo-planar sequence, which, when using comparable image acquisition parameters, noticeably reduces measurement time compared to a conventional WB-DWI protocol. MATERIALS AND METHODS A single-shot echo-planar imaging sequence capable ...

Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

Thanks to the success of deep learning, cross-modal retrieval has made significant progress recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to further enhance the retrieval accuracy. In this paper, we propose a self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal ...

Multi-modal Medical Image Retrieval

Images are ubiquitous in biomedicine and the image viewers play a central role in many aspects of modern health care. Tremendous amounts of medical image data are captured and recorded in digital format during the daily clinical practice, medical research, and education (in 2009, over 117,000 images per day in the Geneva radiology department alone). Facing such an unprecedented volume of image ...