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Found 35 papers, 13 papers with code
How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?
However, the performance of these tasks can be heavily influenced by the variability in textual prompts describing the categories, necessitating robustness in MedVLP models to diverse prompt styles.
Benchmarking and Boosting Radiology Report Generation for 3D High-Resolution Medical Images
Automatic radiology report generation can significantly benefit the labor-intensive process of report writing by radiologists, especially for 3D radiographs like CT scans, which are crucial for broad clinical diagnostics yet underexplored compared to 2D radiographs.
Noise2Noise Denoising of CRISM Hyperspectral Data
Hyperspectral data acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) have allowed for unparalleled mapping of the surface mineralogy of Mars.
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals.
Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
Through multimodal learning on ECG records and associated reports, MERL is capable of performing zero-shot ECG classification with text prompts, eliminating the need for training data in downstream tasks.
MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians.
Explainable Global Wildfire Prediction Models using Graph Neural Networks
Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change.
Multi-fidelity physics constrained neural networks for dynamical systems
As a result, during the training of predictive models, physical constraints can be evaluated within low-fidelity spaces, yielding a trade-off between training efficiency and accuracy.
Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK
In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting.
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training
Modern healthcare often utilises radiographic images alongside textual reports for diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) with large pre-trained models to learn versatile medical vision representations.
T3D: Towards 3D Medical Image Understanding through Vision-Language Pre-training
This underlines T3D's potential in representation learning for 3D medical image analysis.
G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training
G2D achieves superior performance across 6 medical imaging tasks and 25 diseases, particularly in semantic segmentation, which necessitates fine-grained, semantically-grounded image features.
Efficient deep data assimilation with sparse observations and time-varying sensors
We introduce a novel variational DA scheme, named Voronoi-tessellation Inverse operator for VariatIonal Data assimilation (VIVID), that incorporates a DL inverse operator into the assimilation objective function.
IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training
The framework derives multi-level visual features from the chest X-ray (CXR) images and separately aligns these features with the descriptive and the conclusive text encoded in the hierarchical medical report.
Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images
The advent of text-guided generative models raises a compelling question: Can VLP be implemented solely with synthetic images generated from genuine radiology reports, thereby mitigating the need for extensively pairing and curating image-text datasets?
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training
In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool.
A generative model for surrogates of spatial-temporal wildfire nowcasting
The model is tested in the ecoregion of a recent massive wildfire event in California, known as the Chimney fire.
M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization
The proposed method, named Medical vision-language pre-training with Frozen language models and Latent spAce Geometry optimization (M-FLAG), leverages a frozen language model for training stability and efficiency and introduces a novel orthogonality loss to harmonize the latent space geometry.
A novel approach for predicting epidemiological forecasting parameters based on real-time signals and Data Assimilation
This paper proposes a novel approach to predict epidemiological parameters by integrating new real-time signals from various sources of information, such as novel social media-based population density maps and Air Quality data.
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation
To address these challenges, we present Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation (GTGM), a framework that extends of VLP to 3D medical images without relying on paired textual descriptions.
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
Med-UniC reaches superior performance across 5 medical image tasks and 10 datasets encompassing over 30 diseases, offering a versatile framework for unifying multi-modal medical data within diverse linguistic communities.
Frozen Language Model Helps ECG Zero-Shot Learning
In downstream classification tasks, METS achieves around 10% improvement in performance without using any annotated data via zero-shot classification, compared to other supervised and SSL baselines that rely on annotated data.
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement
The robust performance of SCDNN provides a new perspective to exploit knowledge across deep learning models from time and spectral domains.
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models
The new approach is tested on a high-dimensional CFD application of a two-phase liquid flow with non-linear observation operators that current Latent Assimilation methods can not handle.
Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations
Starting from the Kaya identity, we used a Neural ODE model to predict the evolution of several indicators related to carbon emissions, on a country-level: population, GDP per capita, energy intensity of GDP, carbon intensity of energy.
A Scalable Inference Method For Large Dynamic Economic Systems
The nature of available economic data has changed fundamentally in the last decade due to the economy's digitisation.
Correcting public opinion trends through Bayesian data assimilation
Traditional survey polling remains the most popular estimation technique, despite its cost and time intensity, measurement errors, lack of real-time capabilities and lagged representation of public opinion.
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
Our two-step method integrates a Principal Components Analysis (PCA) based adversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM) networks.
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
Here we introduce two digital twins of a SEIRS model applied to an idealised town.
Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation
We propose a new 'Bi-Reduced Space' approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders.
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
This adversarially trained LSTM-based approach is used on the ROM in order to produce faster forecasts of the air pollution tracer.
Data Assimilation in the Latent Space of a Neural Network
There is an urgent need to build models to tackle Indoor Air Quality issue.
An Epidemiological Modelling Approach for Covid19 via Data Assimilation
We compare and discuss model results which conducts updates as new observations become available.
Urban air pollution forecasts generated from latent space representation
Once the PCA is applied on the original model solution, a Fully-Connected AE is trained on the full-rank PCs.
