Search Results for author:
Found 9 papers, 2 papers with code
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data.
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
For the second cohort, follow-up data of 311 patients was used to examine progression prediction capabilities.
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
A human-in-the-loop annotation workflow was employed, leveraging cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomical structures.
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
In this study, we show that a simple image inversion technique can significantly improve the segmentation quality of CT segmentation models on MRI data.
Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports
While recent publications have explored GPT-4 in its application to extracting information of interest from radiology reports, there has not been a real-world comparison of GPT-4 to different leading open-source models.
MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields.
MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain.
What Does DALL-E 2 Know About Radiology?
Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology provided that these models have sufficient medical domain knowledge.
3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware
Our model performed comparable to previously published 3D U-Net architectures, achieving a mean Dice score of 0. 679 on the tuning dataset, 0. 648 on the Coronacases dataset and 0. 405 on the MosMed dataset.
