Latest AI and machine learning research in transplantation for healthcare professionals.
Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify the data-parameter interference as a geometric source of this instability. This interference is controlled by the alignment between LoRA update subspaces and client activations, sugg...
Computed tomography (CT) vision-language pretraining from paired volumes and radiology reports is a scalable yet challenging task. Existing methods commonly adopt global scan-report contrast, which is scalable but obscures heterogeneous organ evidence. Meanwhile, direct organ-level alignment remains coarse, since the same anatomy can exhibit multiple distinct radiological appearances. Therefore, p...
Medical vision-language pretraining (VLP) from paired CT images and radiology reports enables scalable representation learning, but most existing meth...
Geroscience clinical trials need biomarker surrogate endpoints for healthspan. Leading candidates are omics-based composites developed from machine le...
Autism development involves multiple genetic and early-life environmental factors. Studying the placenta's gene expression profile may reveal key mech...
Many biological characteristics arise by interactions between more than one biological organism or unit. Fertilization success in sexually reproducing...
Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error ...
Directed evolution consisting of iterative rounds of diversification, selection, and counter-selection, underlies modern protein and antibody engineer...
Knowledge-based Visual Question Answering (KB-VQA) requires models to retrieve visual entities matching the query image from large-scale encyclopedic ...
Semi-supervised 3D medical image segmentation reduces the need for dense voxel-level annotations by exploiting unlabeled volumes. Although existing me...
Recent advances in generative Artificial Intelligence have made synthetic face images increasingly realistic, creating new challenges for multimedia f...
Blood group antigens, defined by epitopes on the erythrocyte surface, are central to transfusion safety and maternal-fetal compatibility. While the ge...
Unplanned readmissions after liver transplantation occur in over 30% of recipients, yet no validated prediction models exist, and prior observational ...
Introduction: Plasma metagenomic next-generation sequencing (mNGS) may detect pathogens in solid organ transplant (SOT) recipients, but optimal patien...
Intelligent industrial maintenance critically relies on reliable fault diagnosis of rotating machinery. However, it faces formidable challenges from u...
T cell receptor (TCR) recognition is MHC-restricted, yet accurately predicting a TCR's restricting HLA allele remains an open problem. We present TRIO...
The increasing prevalence of neurological disorders highlights the need for human in vitro systems that recapitulate key mechanisms of neurodegenerati...
In vivo CAR-T cell therapy eliminates manufacturing complexities associated with ex vivo autologous approaches, but safety concerns have limited adopt...
Generative models have achieved remarkable progress, yet applying them to satellite imagery remains challenging. Unlike natural imagery, satellite sce...
Text-rich image generation is one of the most challenging settings in image generation, since models must simultaneously produce visually realistic im...