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Found 32 papers, 14 papers with code
Towards Democratization of Subspeciality Medical Expertise
Cardiologists' responses with access to AMIE were superior to cardiologist responses without access to AMIE for all 10 domains.
Advancing Multimodal Medical Capabilities of Gemini
Building upon Gemini's multimodal models, we develop several models within the new Med-Gemini family that inherit core capabilities of Gemini and are optimized for medical use via fine-tuning with 2D and 3D radiology, histopathology, ophthalmology, dermatology and genomic data.
Capabilities of Gemini Models in Medicine
We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin.
Ranked #1 on
Question Answering
on MedQA
(using extra training data)
Towards Conversational Diagnostic AI
The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors.
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow.
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment.
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning
Textural Inversion, a prompt learning method, learns a singular text embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images.
Towards Generalist Biomedical AI
While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems.
Low-field magnetic resonance image enhancement via stochastic image quality transfer
Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field.
Generative models improve fairness of medical classifiers under distribution shifts
In our work, we show that learning realistic augmentations automatically from data is possible in a label-efficient manner using generative models.
Repairing Neural Networks by Leaving the Right Past Behind
Prediction failures of machine learning models often arise from deficiencies in training data, such as incorrect labels, outliers, and selection biases.
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging
We suggest that the quality of the identified failure types can be validated through measuring the intra- and inter-type generalisation after fine-tuning and introduce metrics to compare different subtyping methods.
Learning to Downsample for Segmentation of Ultra-High Resolution Images
Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget.
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.
Disentangling Human Error from Ground Truth in Segmentation of Medical Images
In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large.
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Recent years have seen increasing use of supervised learning methods for segmentation tasks.
Foveation for Segmentation of Ultra-High Resolution Images
We demonstrate on three publicly available high-resolution image datasets that the foveation module consistently improves segmentation performance over the cases trained with patches of fixed FoV/resolution trade-off.
Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients
1. 5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robustness to power failures.
Reproducibility of an airway tapering measurement in CT with application to bronchiectasis
Purpose: This paper proposes a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on CT. We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure.
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
In this paper we propose a probabilistic decimation simulator to improve robustness of model training.
Tapering Analysis of Airways with Bronchiectasis
We propose a simple measurement of tapering along the airways to diagnose and monitor bronchiectasis.
Let's agree to disagree: learning highly debatable multirater labelling
Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time.
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on the design of feature sharing between tasks within the architecture.
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement
Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution.
Multi-Stage Prediction Networks for Data Harmonization
Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison.
Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection
We can compute a relative change in airway volume that may be useful for quantifying IPF disease progression.
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images
In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning.
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources.
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.
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs).
