Latest AI and machine learning research in transplantation for healthcare professionals.
Background: Electronic health records (EHRs) enable machine learning for diagnosis, prognosis, and clinical decision support. However, EHR standards vary by country and hospital, making records often incompatible. This limits large-scale and cross-clinical machine learning. To address such complexity, a metadata repository cataloguing available data elements, their value domains, and their compati...
Diffusion probabilistic models have demonstrated significant potential in generating high-quality, realistic medical images, providing a promising solution to the persistent challenge of data scarcity in the medical field. Nevertheless, producing 3D medical volumes with anatomically consistent structures under multimodal conditions remains a complex and unresolved problem. We introduce Sketch2CT, ...
Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and func...
Abdominal CT data are limited by high annotation costs and privacy constraints, which hinder the development of robust segmentation and diagnostic mod...
Resource-constrained autonomous robots rely on sparse direct and semi-direct visual-(inertial)-odometry (VO) pipelines, as they provide a favorable tr...
Aims We aimed to examine public perceptions of sharing various types of health data relevant for AI development, including electronic health records, ...
Background: Pediatric dilated cardiomyopathy (DCM) is a rare, progressive heart disease with variable outcomes that range from recovery to heart trans...
FireANTs introduced a novel Eulerian descent method for plug-and-play behavior with arbitrary optimizers adapted for diffeomorphic image registration ...
Regional lymph node (LN) metastasis critically influences distant metastatic progression, anti-tumour immunity, and patient prognosis. While tumour-in...
Non-human primates (NHPs), particularly Macaca fascicularis (cynomolgus macaque), represent an essential model for preclinical assessment of biologics...
Speculative Jacobi Decoding (SJD) offers a draft-model-free approach to accelerate autoregressive text-to-image synthesis. However, the high-entropy n...
Accurate multi-organ segmentation in abdominal CT scans is essential for computer-aided diagnosis and treatment. While convolutional neural networks (...
Recent advancements in Gaussian Splatting (3DGS) have introduced various modifications to the original kernel, resulting in significant performance im...
Conventional pixel-wise loss functions fail to enforce topological constraints in coronary vessel segmentation, producing fragmented vascular trees de...
Current neuroscience is shifting from simple controlled paradigms towards rich and ecologically valid naturalistic stimuli. Correspondingly, insights ...
Motivation: Encoding antibodies (Abs) and nanobodies (Nbs) as mRNA enables in vivo production of therapeutic proteins. However, this approach requires...
Harmonization methods such as ComBat and its variants are widely used to mitigate diffusion MRI (dMRI) site-specific biases. However, ComBat assumes t...
Glycosyltransferases (GTs) are a large family of enzymes that catalyze the formation of glycosidic linkages between chemically diverse donor and accep...
Machine learning approaches are increasingly applied to high-dimensional biological data in which features are often dataset-dependent. In many omics ...
While aging manifests differently across organs and individuals, existing approaches to measure it lack the spatial resolution to capture this complex...