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Found 26 papers, 10 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.
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
We investigate the prominent class of fair representation learning methods for bias mitigation.
An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation
Using an off-the-shelf language model, we distil ground-truth reports into radiological descriptions for each SAE feature, which we then compile into a full report for each image, eliminating the need for fine-tuning large models for this task.
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.
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.
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
As machine learning methods gain prominence within clinical decision-making, addressing fairness concerns becomes increasingly urgent.
Measuring axiomatic soundness of counterfactual image models
We present a general framework for evaluating image counterfactuals.
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.
Deep Structural Causal Shape Models
Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association.
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing
We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision--language processing.
Active label cleaning for improved dataset quality under resource constraints
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment of model performance.
Hierarchical Analysis of Visual COVID-19 Features from Chest Radiographs
Chest radiography has been a recommended procedure for patient triaging and resource management in intensive care units (ICUs) throughout the COVID-19 pandemic.
Deep Structural Causal Models for Tractable Counterfactual Inference
We formulate a general framework for building structural causal models (SCMs) with deep learning components.
Causality matters in medical imaging
This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice.
Domain Generalization via Model-Agnostic Learning of Semantic Features
Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions.
Ranked #75 on
Domain Generalization
on PACS
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data.
Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model
Current face recognition systems typically operate via classification into known identities obtained from supervised identity annotations.
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.
From Face Recognition to Models of Identity: A Bayesian Approach to Learning about Unknown Identities from Unsupervised Data
There are two problems with this current paradigm: (1) current systems are unable to benefit from unlabelled data which may be available in large quantities; and (2) current systems equate successful recognition with labelling a given input image.
Semi-Supervised Learning via Compact Latent Space Clustering
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.
Nonparametric Density Flows for MRI Intensity Normalisation
With the adoption of powerful machine learning methods in medical image analysis, it is becoming increasingly desirable to aggregate data that is acquired across multiple sites.
