Latest AI and machine learning research in transplantation for healthcare professionals.
Medical image segmentation commonly relies on U-shaped encoder-decoder architectures such as U-Net, where skip connections preserve fine spatial detail by injecting high-resolution encoder features into the decoder. However, these skip pathways also propagate low-level textures, background clutter, and acquisition noise, allowing irrelevant information to bypass deeper semantic filtering -- an iss...
Background: Sepsis remains a leading cause of preventable hospital mortality in England, with NHS England reporting over 48,000 sepsis-related deaths annually. Natural language processing (NLP)-driven clinical decision support systems (CDSS) have been deployed in several NHS Trusts to enable automated early detection of sepsis from unstructured clinical notes, yet causal evidence of their effectiv...
Structural recognition between a protein target and a ligand underpins therapeutic innovation, yet computational representations of protein binding si...
Millions of experimental and AI-predicted protein structures are now available, and the biosynthetic promise of bespoke proteins is increasingly withi...
Although data generation is often straightforward, extracting information from data is more difficult. Object-centric representation learning can extr...
Vision Transformers (ViTs) excel in 3D medical segmentation but require massive annotated datasets. While Self-Supervised Learning (SSL) mitigates thi...
Accurate rejection of sensitive or harmful visual content, i.e., harmful image guardrail, is critical in many application scenarios. This task must co...
Large unimodal foundation models for vision and language encode rich semantic structures, yet aligning them typically requires computationally intensi...
Vision-language models (VLMs) show strong potential for complex diagnostic tasks in medical imaging. However, applying VLMs to multi-organ medical ima...
Background: Kidney volumetry derived from CT has been proposed as a surrogate of renal function in living kidney donor evaluation. However, clinical i...
A clinically actionable Cardiac Digital Twin (CDT) should reconstruct individualised cardiac anatomy and physiology, update its internal state from mu...
Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging man...
Foundation models increasingly offer potential to support interactive, agentic workflows that assist researchers during analysis and interpretation of...
Accurate prediction of malignancy in renal tumors is crucial for informing clinical decisions and optimizing treatment strategies. However, existing i...
Pose-guided human image animation aims to synthesize realistic videos of a reference character driven by a sequence of poses. While diffusion-based me...
Large-scale, volumetric medical imaging datasets typically aggregate scans from different vendors and devices, resulting in highly variable resolution...
Learning-based 3D visual geometry models have significantly advanced with the advent of large-scale transformers. Among these, StreamVGGT leverages fr...
Artificial intelligence has emerged as a transformative tool in medical image analysis, yet developing robust and generalizable segmentation models re...
Chimeric antigen receptor (CAR) T cell therapies have reshaped treatment for cancers and immune-mediated diseases, yet their safety and efficacy depen...
Fecal microbiota transplantation (FMT) has emerged as a highly effective treatment for recurrent Clostridioides difficile infection and is being activ...