A5064157732- Domain Adaptation and Few-Shot Learning
- Machine Learning and Data Classification
- AI in cancer detection
- Advanced Image Processing Techniques
- Advanced Neural Network Applications
- Artificial Intelligence in Healthcare and Education
- Advanced MRI Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Advanced Neuroimaging Techniques and Applications
- Human Pose and Action Recognition
- Sparse and Compressive Sensing Techniques
- Medical Image Segmentation Techniques
- Adversarial Robustness in Machine Learning
- Multimodal Machine Learning Applications
- Image and Signal Denoising Methods
- Explainable Artificial Intelligence (XAI)
- Medical Imaging Techniques and Applications
- Image Enhancement Techniques
- Model Reduction and Neural Networks
- Machine Learning in Healthcare
- Neural Networks and Applications
- Topic Modeling
- MRI in cancer diagnosis
- Anomaly Detection Techniques and Applications
- Digital Imaging for Blood Diseases
Google (United Kingdom)
2022-2024
DeepMind (United Kingdom)
2022-2024
Google (United States)
2023-2024
Microsoft Research (United Kingdom)
2019-2023
Intel (United Kingdom)
2020-2023
University College London
2016-2022
The predictive performance of supervised learning algorithms depends on the quality labels. In a typical label collection process, multiple annotators provide subjective noisy estimates ``truth" under influence their varying skill-levels and biases. Blindly treating these labels as ground truth limits accuracy in presence strong disagreement. This problem is critical for applications domains such medical imaging where both annotation cost inter-observer variability are high. this work, we...
BackgroundMedicine is inherently multimodal, requiring the simultaneous interpretation and integration of insights between many data modalities spanning text, imaging, genomics, more. Generalist biomedical artificial intelligence systems that flexibly encode, integrate, interpret these might better enable impactful applications ranging from scientific discovery to care delivery.MethodsTo catalyze development models, we curated MultiMedBench, a new multimodal benchmark. MultiMedBench...
At the heart of medicine lies physician-patient dialogue, where skillful history-taking paves way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable diagnostic dialogue could increase accessibility, consistency, quality care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Explorer), a Large Language Model (LLM) based AI system optimized dialogue. uses novel...
Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date knowledge and understanding complex multimodal data. Gemini models, with strong general capabilities long-context offer exciting possibilities medicine. Building on these core strengths Gemini, we introduce Med-Gemini, family highly capable models that are specialized medicine the ability seamlessly use web search, can be efficiently tailored novel...
Abstract Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation some groups or model development common cause this phenomenon. This often not readily addressed by targeted acquisition ‘labeling’ expert clinicians, which can prohibitively expensive practically impossible rarity available...
Diffusion MRI is being used increasingly in studies of the brain and other parts body for its ability to provide quantitative measures that are sensitive changes tissue microstructure. However, inter-scanner inter-protocol differences known induce significant measurement variability, which turn jeopardises obtain ‘truly measures’ challenges reliable combination different datasets. Combining datasets from scanners and/or acquired at time points could dramatically increase statistical power...
This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to the rich information available from one-off experimental medical devices abundant but lower-quality data routine acquisitions. The procedure matched pairs learn mappings low-quality corresponding high-quality images. Once learned, these then augment unseen low images, for example by enhancing resolution or content. Here, we demonstrate using simple patch-regression...
Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different sources uncertainty problems. Here we introduce methods characterise components uncertainty, and demonstrate ideas using diffusion MRI super-resolution. Specifically, propose account for intrinsic through a heteroscedastic noise model parameter approximate...
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have a confounding effect on assessment model performance. Nevertheless, employing experts remove noise by fully re-annotating large datasets is infeasible resource-constrained settings, such healthcare. This work advocates for data-driven approach prioritising samples re-annotation-which we term "active cleaning". We propose rank instances according estimated correctness...
Automated radiology report generation has the potential to improve patient care and reduce workload of radiologists. However, path toward real-world adoption been stymied by challenge evaluating clinical quality artificial intelligence (AI)-generated reports. We build a state-of-the-art system for chest radiographs, called Flamingo-CXR, perform an expert evaluation AI-generated reports engaging panel board-certified observe wide distribution preferences across settings, with 56.1%...
The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on the design feature sharing between tasks within architecture. number possible patterns are combinatorial depth network and tasks, thus hand-crafting an architecture, purely based human intuitions task relationships can be time-consuming suboptimal. In this paper, we present a probabilistic approach to task-specific shared representations CNNs for learning. Specifically, propose "stochastic filter groups"...
Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance these algorithms depends on quality labels. This problem is particularly pertinent in medical image domain, where both annotation cost and inter-observer variability are high. In a typical label acquisition process, different human experts provide their estimates "true" labels under influence own biases competence levels. Treating noisy blindly as ground truth...
Supervised machine learning methods have been widely developed for segmentation tasks in recent years. However, the quality of labels has high impact on predictive performance these algorithms. This issue is particularly acute medical image domain, where both cost annotation and inter-observer variability are high. Different human experts contribute estimates "actual" a typical label acquisition process, influenced by their personal biases competency levels. The automatic algorithms limited...
Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Building upon Gemini's models, we develop several models within the new Med-Gemini family that inherit core capabilities Gemini are optimized for use via fine-tuning with 2D 3D radiology, histopathology, ophthalmology, dermatology genomic data. Med-Gemini-2D sets a standard AI-based chest X-ray (CXR) report generation...
Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, interpret this at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To the development of these models, we first curate MultiMedBench, a new multimodal benchmark. MultiMedBench encompasses 14 diverse tasks such as medical question answering,...
We introduce Lavender, a simple supervised fine-tuning (SFT) method that boosts the performance of advanced vision-language models (VLMs) by leveraging state-of-the-art image generation such as Stable Diffusion. Specifically, Lavender aligns text-vision attention in VLM transformer with equivalent used Diffusion during SFT, instead adapting separate encoders. This alignment enriches model's visual understanding and significantly across in- out-of-distribution tasks. requires just 0.13...
Abstract At the heart of medicine lies physician–patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust 1,2 . Artificial intelligence (AI) systems capable diagnostic dialogue could increase accessibility quality care. However, approximating clinicians’ expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for dialogue. uses...
Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or synthesis. However, to date, little consideration been given uncertainty quantification over the output image. Here we introduce methods characterise different components of problems and demonstrate ideas using diffusion MRI super-resolution. Specifically, propose account for $intrinsic$ through a heteroscedastic noise model $parameter$ approximate Bayesian inference, integrate two...