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Found 17 papers, 3 papers with code
RT-Surv: Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records
Survival prediction models were developed using statistical, machine learning, and deep learning approaches, incorporating both structured and LLM-structured data.
Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning
In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches.
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model
As advancements in the field of breast cancer treatment continue to progress, the assessment of post-surgical cosmetic outcomes has gained increasing significance due to its substantial impact on patients' quality of life.
End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding
Inspired by this, here we present RO-LMM, a multi-purpose, comprehensive large multimodal model (LMM) tailored for the field of radiation oncology.
LLM-driven Multimodal Target Volume Contouring in Radiation Oncology
Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates the utilization of both image and text-based clinical information.
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model
Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Single-round Self-supervised Distributed Learning using Vision Transformer
Despite the recent success of deep learning in the field of medicine, the issue of data scarcity is exacerbated by concerns about privacy and data ownership.
Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in Radiology
Recent advances in vision-language models sheds a light on the long-standing problems of the oversight AI by the understanding both visual and textual concepts and their semantic correspondences.
Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation
The widespread application of artificial intelligence in health research is currently hampered by limitations in data availability.
AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation
Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust deep learning model requires large, high-quality data with manual annotation, which is expensive to obtain.
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification
This situation is ideally suited for the Vision Transformer (ViT) architecture, where a lot of unlabeled data can be used through structural modeling by the self-attention mechanism.
Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer
Under the global pandemic of COVID-19, building an automated framework that quantifies the severity of COVID-19 and localizes the relevant lesion on chest X-ray images has become increasingly important.
Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus
Under the global COVID-19 crisis, developing robust diagnosis algorithm for COVID-19 using CXR is hampered by the lack of the well-curated COVID-19 data set, although CXR data with other disease are abundant.
Deep Learning COVID-19 Features on CXR using Limited Training Data Sets
Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important.
