20 research outputs found
Denoising diffusion models for out-of-distribution detection
Out-of-distribution detection is crucial to the safe deployment of machine
learning systems. Currently, unsupervised out-of-distribution detection is
dominated by generative-based approaches that make use of estimates of the
likelihood or other measurements from a generative model. Reconstruction-based
methods offer an alternative approach, in which a measure of reconstruction
error is used to determine if a sample is out-of-distribution. However,
reconstruction-based approaches are less favoured, as they require careful
tuning of the model's information bottleneck - such as the size of the latent
dimension - to produce good results. In this work, we exploit the view of
denoising diffusion probabilistic models (DDPM) as denoising autoencoders where
the bottleneck is controlled externally, by means of the amount of noise
applied. We propose to use DDPMs to reconstruct an input that has been noised
to a range of noise levels, and use the resulting multi-dimensional
reconstruction error to classify out-of-distribution inputs. We validate our
approach both on standard computer-vision datasets and on higher dimension
medical datasets. Our approach outperforms not only reconstruction-based
methods, but also state-of-the-art generative-based approaches. Code is
available at https://github.com/marksgraham/ddpm-ood
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Feature attribution (FA), or the assignment of class-relevance to different
locations in an image, is important for many classification problems but is
particularly crucial within the neuroscience domain, where accurate mechanistic
models of behaviours, or disease, require knowledge of all features
discriminative of a trait. At the same time, predicting class relevance from
brain images is challenging as phenotypes are typically heterogeneous, and
changes occur against a background of significant natural variation. Here, we
present a novel framework for creating class specific FA maps through
image-to-image translation. We propose the use of a VAE-GAN to explicitly
disentangle class relevance from background features for improved
interpretability properties, which results in meaningful FA maps. We validate
our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing
(UK Biobank), and (simulated) lesion detection. We show that FA maps generated
by our method outperform baseline FA methods when validated against ground
truth. More significantly, our approach is the first to use latent space
sampling to support exploration of phenotype variation. Our code will be
available online at https://github.com/CherBass/ICAM.Comment: Submitted to NeurIPS 2020: Neural Information Processing Systems.
Keywords: interpretable, classification, feature attribution, domain
translation, variational autoencoder, generative adversarial network,
neuroimagin
Hierarchical brain parcellation with uncertainty
Many atlases used for brain parcellation are hierarchically organised,
progressively dividing the brain into smaller sub-regions. However,
state-of-the-art parcellation methods tend to ignore this structure and treat
labels as if they are `flat'. We introduce a hierarchically-aware brain
parcellation method that works by predicting the decisions at each branch in
the label tree. We further show how this method can be used to model
uncertainty separately for every branch in this label tree. Our method exceeds
the performance of flat uncertainty methods, whilst also providing decomposed
uncertainty estimates that enable us to obtain self-consistent parcellations
and uncertainty maps at any level of the label hierarchy. We demonstrate a
simple way these decision-specific uncertainty maps may be used to provided
uncertainty-thresholded tissue maps at any level of the label tree.Comment: To be published in the MICCAI 2020 workshop: Uncertainty for Safe
Utilization of Machine Learning in Medical Imagin
Can segmentation models be trained with fully synthetically generated data?
In order to achieve good performance and generalisability, medical image
segmentation models should be trained on sizeable datasets with sufficient
variability. Due to ethics and governance restrictions, and the costs
associated with labelling data, scientific development is often stifled, with
models trained and tested on limited data. Data augmentation is often used to
artificially increase the variability in the data distribution and improve
model generalisability. Recent works have explored deep generative models for
image synthesis, as such an approach would enable the generation of an
effectively infinite amount of varied data, addressing the generalisability and
data access problems. However, many proposed solutions limit the user's control
over what is generated. In this work, we propose brainSPADE, a model which
combines a synthetic diffusion-based label generator with a semantic image
generator. Our model can produce fully synthetic brain labels on-demand, with
or without pathology of interest, and then generate a corresponding MRI image
of an arbitrary guided style. Experiments show that brainSPADE synthetic data
can be used to train segmentation models with performance comparable to that of
models trained on real data.Comment: 12 pages, 2 (+2 App.) figures, 3 tables. Accepted at Simulation and
Synthesis in Medical Imaging workshop (MICCAI 2022
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Human anatomy, morphology, and associated diseases can be studied using
medical imaging data. However, access to medical imaging data is restricted by
governance and privacy concerns, data ownership, and the cost of acquisition,
thus limiting our ability to understand the human body. A possible solution to
this issue is the creation of a model able to learn and then generate synthetic
images of the human body conditioned on specific characteristics of relevance
(e.g., age, sex, and disease status). Deep generative models, in the form of
neural networks, have been recently used to create synthetic 2D images of
natural scenes. Still, the ability to produce high-resolution 3D volumetric
imaging data with correct anatomical morphology has been hampered by data
scarcity and algorithmic and computational limitations. This work proposes a
generative model that can be scaled to produce anatomically correct,
high-resolution, and realistic images of the human brain, with the necessary
quality to allow further downstream analyses. The ability to generate a
potentially unlimited amount of data not only enables large-scale studies of
human anatomy and pathology without jeopardizing patient privacy, but also
significantly advances research in the field of anomaly detection, modality
synthesis, learning under limited data, and fair and ethical AI. Code and
trained models are available at: https://github.com/AmigoLab/SynthAnatomy.Comment: 13 pages, 3 figures, 2 tables, accepted at SASHIMI MICCAI 202
Transformer-based out-of-distribution detection for clinically safe segmentation
In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are far outside the training data distribution. Recently, generative modelling approaches have been proposed as an alternative; these can quantify the likelihood of a data sample explicitly, filtering out any out-of-distribution (OOD) samples before further processing is performed. In this work, we focus on image segmentation and evaluate several approaches to network uncertainty in the far-OOD and near-OOD cases for the task of segmenting haemorrhages in head CTs. We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD. We propose performing full 3D OOD detection using a VQ-GAN to provide a compressed latent representation of the image and a transformer to estimate the data likelihood. Our approach successfully identifies images in both the far- and near-OOD cases. We find a strong relationship between image likelihood and the quality of a model’s segmentation, making this approach viable for filtering images unsuitable for segmentation. To our knowledge, this is the first time transformers have been applied to perform OOD detection on 3D image data.</p
ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans
An important goal of medical imaging is to be able to precisely detect
patterns of disease specific to individual scans; however, this is challenged
in brain imaging by the degree of heterogeneity of shape and appearance.
Traditional methods, based on image registration to a global template,
historically fail to detect variable features of disease, as they utilise
population-based analyses, suited primarily to studying group-average effects.
In this paper we therefore take advantage of recent developments in generative
deep learning to develop a method for simultaneous classification, or
regression, and feature attribution (FA). Specifically, we explore the use of a
VAE-GAN translation network called ICAM, to explicitly disentangle class
relevant features from background confounds for improved interpretability and
regression of neurological phenotypes. We validate our method on the tasks of
Mini-Mental State Examination (MMSE) cognitive test score prediction for the
Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age
prediction, for both neurodevelopment and neurodegeneration, using the
developing Human Connectome Project (dHCP) and UK Biobank datasets. We show
that the generated FA maps can be used to explain outlier predictions and
demonstrate that the inclusion of a regression module improves the
disentanglement of the latent space. Our code is freely available on Github
https://github.com/CherBass/ICAM
ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans
Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification and regression problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present an extension of the ICAM framework for creating prediction specific FA maps through image-to-image translation
Latent Transformer Models for out-of-distribution detection
Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and are not presented to the downstream predictive model. In this work, we evaluate several approaches to segmentation with uncertainty for the task of segmenting bleeds in 3D CT of the head. We show that these models can fail catastrophically when operating in the far out-of-distribution domain, often providing predictions that are both highly confident and wrong. We propose to instead perform out-of-distribution detection using the Latent Transformer Model: a VQ-GAN is used to provide a highly compressed latent representation of the input volume, and a transformer is then used to estimate the likelihood of this compressed representation of the input. We demonstrate this approach can identify images that are both far- and near- out-of-distribution, as well as provide spatial maps that highlight the regions considered to be out-of-distribution. Furthermore, we find a strong relationship between an image's likelihood and the quality of a model's segmentation on it, demonstrating that this approach is viable for filtering out unsuitable images
Generative AI for Medical Imaging: extending the MONAI Framework
Recent advances in generative AI have brought incredible breakthroughs in
several areas, including medical imaging. These generative models have
tremendous potential not only to help safely share medical data via synthetic
datasets but also to perform an array of diverse applications, such as anomaly
detection, image-to-image translation, denoising, and MRI reconstruction.
However, due to the complexity of these models, their implementation and
reproducibility can be difficult. This complexity can hinder progress, act as a
use barrier, and dissuade the comparison of new methods with existing works. In
this study, we present MONAI Generative Models, a freely available open-source
platform that allows researchers and developers to easily train, evaluate, and
deploy generative models and related applications. Our platform reproduces
state-of-art studies in a standardised way involving different architectures
(such as diffusion models, autoregressive transformers, and GANs), and provides
pre-trained models for the community. 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. Finally, we adopt a
modular and extensible approach, ensuring long-term maintainability and the
extension of current applications for future features