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Found 67 papers, 36 papers with code
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
Measuring the Biases and Effectiveness of Content-Style Disentanglement
In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance.
Diffusion Causal Models for Counterfactual Estimation
We consider the task of counterfactual estimation from observational imaging data given a known causal structure.
What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
This requires training with healthy and unhealthy data in DPMs.
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
Maximisation of mutual information is achieved by introducing an auxiliary network and training with a latent regression loss.
Diffusion Models for Causal Discovery via Topological Ordering
We introduce theory for updating the learned Hessian without re-training the neural network, and we show that computing with a subset of samples gives an accurate approximation of the ordering, which allows scaling to datasets with more samples and variables.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Learning Disentangled Representations in the Imaging Domain
Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision.
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks.
Disentangled Representation Learning in Cardiac Image Analysis
We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
We explicitly model the representations related to domain shifts.
The role of noise in denoising models for anomaly detection in medical images
Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance.
Learning to synthesise the ageing brain without longitudinal data
Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable).
Blind Inpainting of Large-scale Masks of Thin Structures with Adversarial and Reinforcement Learning
We evaluate our method in several datasets in medical imaging, plant science, and remote sensing.
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI.
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Core to our method is learning a disentangled decomposition into anatomical and imaging factors.
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
OMASGAN addresses the rarity of anomalies by generating strong and adversarial OoD samples on the distribution boundary using only normal class data, effectively addressing mode collapse.
vMFNet: Compositionality Meets Domain-generalised Segmentation
Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image.
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whilst relying less on annotated data (since annotations can be tedious and costly).
Disentangled Representations for Domain-generalized Cardiac Segmentation
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains.
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Together with the corresponding encoding features, these representations are propagated to decoding layers with U-Net skip-connections.
INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs
We evaluate the method on two datasets: a new CLEVR-Seg dataset where we segment objects based on location, and the ACDC dataset conditioned on cardiac phase and slice location within the volume.
Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts
After training is complete, the discriminator is usually discarded, and only the generator is used for inference.
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training
At inference, the discriminator is discarded, and only the segmentor is used to predict label maps on test images.
Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers
We use the indication field to drive better image classification, by taking a transformer network which is unimodally pre-trained on text (BERT) and fine-tuning it for multimodal classification of a dual image-text input.
Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning
Collecting large-scale medical datasets with fine-grained annotations is time-consuming and requires experts.
Pseudo-healthy synthesis with pathology disentanglement and adversarial learning
In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy.
Semi-supervised Pathology Segmentation with Disentangled Representations
The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations.
Controllable cardiac synthesis via disentangled anatomy arithmetic
Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed "disentangled anatomy arithmetic", in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e. g. MRI), plausible new cardiac images are created with the target characteristics.
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
We demonstrate empirically that FairTune leads to improved fairness on a range of medical imaging datasets.
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner.
Inference Stage Denoising for Undersampled MRI Reconstruction
In this work, by employing a conditional hyperparameter network, we eliminate the need of augmentation, yet maintain robust performance under various levels of Gaussian noise.
Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction
Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time.
Benchmarking Counterfactual Image Generation
Counterfactual image generation is pivotal for understanding the causal relations of variables, with applications in interpretability and generation of unbiased synthetic data.
Leveraging multiple datasets for deep leaf counting
While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation).
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
We show that our model is able to generate realistic 128x128 colour images of plants.
Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations
In this paper, we propose the Theta-Restricted Boltzmann Machine ({\theta}-RBM in short), which builds upon the original RBM formulation and injects the notion of rotation-invariance during the learning procedure.
Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters
Finding suitable features has been an essential problem in computer vision.
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization
Pseudo healthy synthesis, i. e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation.
The Generalized Complex Kernel Least-Mean-Square Algorithm
Also, the flexibility of the proposed generalized approach is tested in a second experiment with non-independent real and imaginary parts.
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway.
Teacher-Student chain for efficient semi-supervised histology image classification
Deep learning shows great potential for the domain of digital pathology.
Have you forgotten? A method to assess if machine learning models have forgotten data
In the era of deep learning, aggregation of data from several sources is a common approach to ensuring data diversity.
Survey: Leakage and Privacy at Inference Time
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data.
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
We propose an invertible-residual-network-based model, the Boundary of Distribution Support Generator (BDSG).
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation
In this paper, we create a GAN-based tail formation model for anomaly detection, the Tail of distribution GAN (TailGAN), to generate samples on the tail of the data distribution and detect anomalies near the support boundary.
Generative Adversarial Network
Unsupervised Anomaly Detection
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection
We propose a self-supervised learning few-shot confidence boundary methodology based on generative and discriminative models, including classification.
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection
By including our boundary, FROB reduces the threshold linked to the model's few-shot robustness; it maintains the OoD performance approximately independent of the number of few-shots.
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
Measuring Unintended Memorisation of Unique Private Features in Neural Networks
Neural networks pose a privacy risk to training data due to their propensity to memorise and leak information.
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification
To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as a downstream task.
Unintended memorisation of unique features in neural networks
Neural networks pose a privacy risk due to their propensity to memorise and leak training data.
Causal Machine Learning for Healthcare and Precision Medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Why patient data cannot be easily forgotten?
With the advent of AI learned on data, one can imagine that such rights can extent to requests for forgetting knowledge of patient's data within AI models.
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
This is particularly important in the context of medical image segmentation of pathological structures (e. g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others.
Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e. g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT) techniques for diverse medical image analysis tasks.
Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself.
Compositionally Equivariant Representation Learning
By modelling the compositional representations with learnable von-Mises-Fisher (vMF) kernels, we explore how different design and learning biases can be used to enforce the representations to be more compositionally equivariant under un-, weakly-, and semi-supervised settings.
A Causal Ordering Prior for Unsupervised Representation Learning
Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables.
Compositional Representation Learning for Brain Tumour Segmentation
For brain tumour segmentation, deep learning models can achieve human expert-level performance given a large amount of data and pixel-level annotations.
Group Distributionally Robust Knowledge Distillation
Knowledge distillation enables fast and effective transfer of features learned from a bigger model to a smaller one.
Adapting Vision Foundation Models for Plant Phenotyping
The experimental results show that a foundation model can be efficiently adapted to multiple plant phenotyping tasks, yielding similar performance as the state-of-the-art (SoTA) models specifically designed or trained for each task.
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data.
