Related papers: NEMAR: An open access data, tools, and compute res…
Accurate and efficient analysis of materials properties from Nuclear Magnetic Resonance (NMR) relaxation data requires robust and efficient inversion procedures. Despite the great variety of applications requiring to process two-dimensional…
With the arrival of the big data era, more and more data are becoming readily available in various real-world applications and those data are usually highly heterogeneous. Taking computational medicine as an example, we have both Electronic…
Objective. Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is often…
We present a system that constructs and maintains an up-to-date co-occurrence network of medical concepts based on continuously mining the latest biomedical literature. Users can explore this network visually via a concise online interface…
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There…
open Master Hearing Aid (openMHA) was developed and provided to the hearing aid research community as an open-source software platform with the aim to support sustainable and reproducible research towards improvement and new types of…
X-ray images ease the diagnosis and treatment process due to their rapid imaging speed and high resolution. However, due to the projection process of X-ray imaging, much spatial information has been lost. To accurately provide efficient…
The rapid developments in artificial intelligence (AI) research in radiology have produced numerous models that are scattered across various platforms and sources, limiting discoverability, reproducibility and clinical translation. Herein,…
In the rapidly evolving field of bioimaging, the integration and orchestration of Findable, Accessible, Interoperable, and Reusable (FAIR) image analysis workflows remains a challenge. We introduce BIOMERO, a bridge connecting OMERO, a…
Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing multiple modalities of sensory data (e.g. video, audio) at…
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices. Despite the significant progress in NN model compression, there has been considerably less…
This paper addresses both the various EEG applications and the current EEG market ecosystem propelled by machine learning. Increasingly available open medical and health datasets using EEG encourage data-driven research with a promise of…
Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the…
Access to clinical multi-channel EEG remains limited in many regions worldwide. We present NEUROSKY-EPI, the first open dataset of single-channel, consumer-grade EEG for epilepsy, collected in a South Asian clinical setting along with rich…
Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately,…
Background: For newborn infants in critical care, continuous monitoring of brain function can help identify infants at-risk of brain injury. Quantitative features allow a consistent and reproducible approach to EEG analysis, but only when…
With the widespread application of electroencephalography (EEG) in neuroscience and clinical practice, efficiently retrieving and semantically interpreting large-scale, multi-source, heterogeneous EEG data has become a pressing challenge.…
In recent years, deep learning revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present…
In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical…
Public data archives are the backbone of modern biological and biomedical research. While archives for biological molecules and structures are well-established, resources for imaging data do not yet cover the full range of spatial and…