Search Results for author:
Found 19 papers, 5 papers with code
MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models
Subsequently, building on the architectures of MAIRA, a CXR-specialised model for report generation, we integrate a trainable segmentation tokens extractor that leverages these mask pseudolabels, and employ mask-aware prompting to generate draft radiology reports.
PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation
Grounded radiology report generation (GRRG) extends RRG by including the localisation of individual findings on the image.
MAIRA-2: Grounded Radiology Report Generation
Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solution.
Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology
Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication.
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.
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).
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.
Looking for Out-of-Distribution Environments in Multi-center Critical Care Data
Clinical machine learning models show a significant performance drop when tested in settings not seen during training.
Early prediction of respiratory failure in the intensive care unit
The development of respiratory failure is common among patients in intensive care units (ICU).
ML4H Abstract Track 2020
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020.
An Empirical Study on the Intrinsic Privacy of SGD
Introducing noise in the training of machine learning systems is a powerful way to protect individual privacy via differential privacy guarantees, but comes at a cost to utility.
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4, 007 cancer patients and their tumors.
Machine learning for early prediction of circulatory failure in the intensive care unit
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.
Improving Clinical Predictions through Unsupervised Time Series Representation Learning
In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making.
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa.
Neural Document Embeddings for Intensive Care Patient Mortality Prediction
We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes.
Learning Unitary Operators with Help From u(n)
A major challenge in the training of recurrent neural networks is the so-called vanishing or exploding gradient problem.
Knowledge Transfer with Medical Language Embeddings
Identifying relationships between concepts is a key aspect of scientific knowledge synthesis.
A Generative Model of Words and Relationships from Multiple Sources
We propose a generative model which integrates evidence from diverse data sources, enabling the sharing of semantic information.
