Latest AI and machine learning research in covid-19 for healthcare professionals.
Conflicting results from randomized trials regarding the efficacy of remdesivir for COVID-19 have been reported. We aimed to develop a neural network (NN) to identify COVID-19 patients who would derive the greatest survival benefit from remdesivir. This multicenter observational study included adults hospitalized for COVID-19 between February 2020 and February 2021. A derivation cohort from Hospit...
Feature attribution methods from explainable artificial intelligence (XAI) provide explanations of machine learning models by quantifying feature importance for predictions of test instances. While features determining individual predictions have frequently been identified in machine learning applications, the consistency of feature importance-based explanations of machine learning models using di...
Magnetic Resonance Imaging (MRI) Super-Resolution (SR) addresses the challenges such as long scan times and expensive equipment by enhancing image r...
While deep learning has significantly advanced medical image segmentation, most existing methods still struggle with handling complex anatomical reg...
Amyotrophic Lateral Sclerosis (ALS) constitutes a progressive neurodegenerative disease with varying symptoms, including decline in speech intelligi...
Vertical Federated Learning (VFL) enables collaborative training with feature-partitioned data, yet remains vulnerable to privacy leakage through gr...
In the last years, especially since the COVID-19 pandemic, precision medicine platforms emerged as useful tools for supporting new tests like the on...
Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an object...
Point cloud upsampling aims to generate dense and uniformly distributed point sets from sparse point clouds. Existing point cloud upsampling methods...
Cross-dataset testing is critical for examining machine learning (ML) model's performance. However, most studies on modelling transcriptomic and cli...
Masked language modelling (MLM) as a pretraining objective has been widely adopted in genomic sequence modelling. While pretrained models can succes...
Precise identification and localization of disease-specific features at the pixel-level are particularly important for early diagnosis, disease prog...
Fr\'echet Inception Distance (FID), computed with an ImageNet pretrained Inception-v3 network, is widely used as a state-of-the-art evaluation metri...
Real-world image matting is essential for applications in content creation and augmented reality. However, it remains challenging due to the complex...
Generative model based compact video compression is typically operated within a relative narrow range of bitrates, and often with an emphasis on ult...
Our goal in this paper is to clarify the relationship between the block Lanczos and the block conjugate gradient (BCG) algorithms. Under the full ra...
In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the p...
Machine learning algorithms are used in diverse domains, many of which face significant challenges due to data imbalance. Studies have explored vari...
Achieving a consistent and compact 3D segmentation field is crucial for maintaining semantic coherence across views and accurately representing scen...
Testing processes usually aim at high coverage, but loops severely limit coverage ambitions since the number of iterations is generally not predicta...