Transplantation

HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation

With the advancement of language models, unified multimodal understanding and generation have made significant strides, with model architectures evolving from separated components to unified single-model frameworks. This paper explores an efficient training paradigm to build a single transformer for unified multimodal understanding and generation. Specifically, we propose a multimodal warmup str...

FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models

Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raising concerns about data scarcity and the lack of access to domain-specific, sensitive information. Federated Learning (FL) presents a compelling framework to address these challenges by enabling decentralized fine-tuning...

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models

Vision-language models, such as CLIP, have achieved significant success in aligning visual and textual representations, becoming essential component...

HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification

Graph Neural Networks (GNNs) have recently been found to excel in histopathology. However, an important histopathological task, where GNNs have not ...

Minimal Impact ControlNet: Advancing Multi-ControlNet Integration

With the advancement of diffusion models, there is a growing demand for high-quality, controllable image generation, particularly through methods th...

A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learning

Purpose: Accurate segmentation of clinical target volumes (CTV) and organs-at-risk is crucial for optimizing gynecologic brachytherapy (GYN-BT) trea...

Taming LLMs by Scaling Learning Rates with Gradient Grouping

Training large language models (LLMs) poses challenges due to their massive scale and heterogeneous architectures. While adaptive optimizers like Ad...

Advancements in Organoid-Based Drug Discovery: Revolutionizing Precision Medicine and Pharmacology.

Organoids, 3D cellular models derived from stem cells, have revolutionized drug testing by providing human-relevant systems for modeling diseases and ...

Revolution of AAV in Drug Discovery: From Delivery System to Clinical Application.

Adeno-associated virus (AAV) is a non-enveloped DNA virus infecting a wide variety of species, tissues, and cell types, which is recognized as a safe ...

Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing.

This research presents an enhanced predictive model for green sand casting, designed to tackle the nonlinear complexities arising from interdependent ...

Transcriptome Derived Artificial neural networks predict PRRC2A as a potent biomarker for epilepsy.

Epilepsy refers to the occurrence of two or more than two reiterative seizures. The occurrence of seizure is governed by the excessive electrical disc...

Modality-Aware Distillation Network for Microvascular Invasion Prediction of Hepatocellar Carcinoma From MRI Images.

Microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is a crucial histopathologic prognostic factor associated with cancer recurrence after ...

Fast Virtual Stenting for Thoracic Endovascular Aortic Repair of Aortic Dissection Using Graph Deep Learning.

Fast virtual stenting (FVS) is a promising preoperative planning aid for thoracic endovascular aortic repair (TEVAR) of aortic dissection. It aims at ...

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital h...

Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.

INTRODUCTION: Sarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as...

Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection.

PURPOSE OF REVIEW: The objective of this review is to provide an update on the application of artificial intelligence (AI) for the histological interp...

Establishment of a machine learning-based prediction framework to assess trade-offs in decisions that affect post-HCT outcomes.

In this study, we propose a conceptual framework of decision support tools, built upon machine learning and multi-objective optimization, aimed at off...

Predicting the solubility of drugs in supercritical carbon dioxide using machine learning and atomic contribution.

The pharmaceutical sector is aware of supercritical CO (SC-CO) as a possible replacement for problematic organic solvents. Using a novel artificial in...

Paving the way for bacteria-based drug delivery: biohybrid microrobots emerging from microrobotics and synthetic biology.

Advances in microrobotics and synthetic biology are paving the way for innovative solutions to long-standing challenges in drug delivery. Both fields ...

A machine learning multimodal profiling of Per- and Polyfluoroalkyls (PFAS) distribution across animal species organs via clustering and dimensionality reduction techniques.

Per- and polyfluoroalkyl substances (PFAS) contamination in aquatic and terrestrial organisms poses significant environmental and health risks. This s...