Latest AI and machine learning research in transplantation for healthcare professionals.
This article methodologically reflects on how social media scholars can effectively engage with speech-based data in their analyses. While contemporary media studies have embraced textual, visual, and relational data, the aural dimension remained comparatively under-explored. Building on the notion of secondary orality and rejection towards purely visual culture, the paper argues that considerin...
Hemodialysis patients who are on donor lists for kidney transplant may get misidentified, delaying their wait time. Thus, predicting their survival time is crucial for optimizing waiting lists and personalizing treatment plans. Predicting survival times for patients often requires large quantities of high quality but sensitive data. This data is siloed and since individual datasets are smaller a...
Medical image colorization can greatly enhance the interpretability of the underlying imaging modality and provide insights into human anatomy. The ...
Recent advances in text-to-image customization have enabled high-fidelity, context-rich generation of personalized images, allowing specific concept...
Modern sensors produce increasingly rich streams of high-resolution data. Due to resource constraints, machine learning systems discard the vast maj...
Patient life circumstances, including social determinants of health (SDOH), shape both health outcomes and care access, contributing to persistent d...
We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machin...
We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image. Balancing real-time speed against high-quality resul...
Large Language Models (LLMs) have been widely used in various tasks, motivating us to develop an LLM-based assistant for videos. Instead of training...
Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision...
How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment methods do n...
This paper introduces UnZipLoRA, a method for decomposing an image into its constituent subject and style, represented as two distinct LoRAs (Low-Ra...
Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a majo...
Pathological cell semantic segmentation is a fundamental technology in computational pathology, essential for applications like cancer diagnosis and...
We present a novel approach to reconstruction of 3D cardiac motion from sparse intraoperative data. While existing methods can accurately reconstruc...
Adrenoleukodystrophy is a genetic metabolic disorder characterized by a heterogeneous phenotype. Its severe form, known as cerebral adrenoleukodystrop...
Convolutional neural networks (CNNs) are well established in handling local features in visual tasks; yet, they falter in managing complex spatial rel...
BACKGROUND: Machine learning has emerged as a potent tool in healthcare. A decision tree model was built to improve the decision-making process when d...
Polymers play a crucial role in the development of engineering materials, with applications ranging from mechanical to biomedical fields. However, t...
BACKGROUND: Outcome prediction for live-donor kidney transplantation improves clinical and patient decisions and donor selection. However, the current...