Search Results for author:
Found 12 papers, 5 papers with code
RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision
We introduce RAD-DINO, a biomedical image encoder pre-trained solely on unimodal biomedical imaging data that obtains similar or greater performance than state-of-the-art biomedical language supervised models on a diverse range of benchmarks.
RadEdit: stress-testing biomedical vision models via diffusion image editing
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing.
MAIRA-1: A specialised large multimodal model for radiology report generation
We present a radiology-specific multimodal model for the task for generating radiological reports from chest X-rays (CXRs).
Exploring the Boundaries of GPT-4 in Radiology
In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models.
Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query
We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomical regions.
Compositional Zero-Shot Domain Transfer with Text-to-Text Models
We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning.
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images.
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike.
Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures
We demonstrate that an STCNN trained on a HAR dataset can be used in combination with an RNN to accurately represent arbitrarily long videos of seizures.
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
We fine-tuned our model on three small annotated datasets from different institutions and on the annotated images in EPISURG, comprising 20, 33, 19 and 133 subjects.
Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning
In addition to EPISURG, we used three public datasets comprising 1813 preoperative MR images for training.
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Additionally, we provide a graphical interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms.
