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Found 16 papers, 8 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.
Identifying treatment response subgroups in observational time-to-event data
In experiments, our approach significantly outperforms the current state-of-the-art method for subgroup analysis in both randomised and observational treatment regimes.
A Critical Review of Causal Reasoning Benchmarks for Large Language Models
We hope this work will pave the way towards a general framework for the assessment of causal understanding in LLMs and the design of novel benchmarks.
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.
Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
In-context learning (ICL) has emerged as a particularly remarkable characteristic of Large Language Models (LLM): given a pretrained LLM and an observed dataset, LLMs can make predictions for new data points from the same distribution without fine-tuning.
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection
In this work, we analyse using the gradient of a data point with respect to the parameters of the deep generative model for OOD detection, based on the simple intuition that OOD data should have larger gradient norms than training data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Unified Framework for U-Net Design and Analysis
U-Nets are a go-to, state-of-the-art neural architecture across numerous tasks for continuous signals on a square such as images and Partial Differential Equations (PDE), however their design and architecture is understudied.
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied.
Neural Score Matching for High-Dimensional Causal Inference
We leverage these balancing scores to perform matching for high-dimensional causal inference and call this procedure neural score matching.
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021.
Multi-Facet Clustering Variational Autoencoders
Work in deep clustering focuses on finding a single partition of data.
Ivy: Templated Deep Learning for Inter-Framework Portability
We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks.
ML4H Abstract Track 2020
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020.
Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM
Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels.
ML4H Abstract Track 2019
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019.
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition.
