1,185 research outputs found
Detection of anatomical structures in medical datasets
Detection and localisation of anatomical structures is extremely helpful for many image analysis algorithms. This thesis is concerned with the automatic identification of landmark points, anatomical regions and vessel centre lines in three-dimensional medical datasets. We examine how machine learning and atlas-based ideas may be combined to produce efficient, context-aware algorithms. For the problem of anatomical landmark detection, we develop an analog to the idea of autocontext, termed atlas location autocontext, whereby spatial context is iteratively learnt by the machine learning algorithm as part of a feedback loop. We then extend our anatomical landmark detection algorithm from Computed Tomography to Magnetic Resonance images, using image features based on histograms of oriented gradients. A cross-modality landmark detector is demonstrated using unsigned gradient orientations. The problem of brain parcellation is approached by independently training a random forest and a multi-atlas segmentation algorithm, then combining them by a simple Bayesian product operation. It is shown that, given classifiers providing complementary information, the hybrid classifier provides a superior result. The Bayesian product method of combination outperforms simple averaging where the classifiers are sufficiently independent. Finally, we present a system for identifying and tracking major arteries in Magnetic Resonance Angiography datasets, using automatically detected vascular landmarks to seed the tracking. Knowledge of individual vessel characteristics is employed to guide the tracking algorithm by two means. Firstly, the data is pre-processed using a top-hat transform of size corresponding to the vessel diameter. Secondly, a vascular atlas is generated to inform the cost function employed in the minimum path algorithm. Fully automatic tracking of the major arteries of the body is satisfactorily demonstrated
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
Generalising deep models to new data from new centres (termed here domains)
remains a challenge. This is largely attributed to shifts in data statistics
(domain shifts) between source and unseen domains. Recently, gradient-based
meta-learning approaches where the training data are split into meta-train and
meta-test sets to simulate and handle the domain shifts during training have
shown improved generalisation performance. However, the current fully
supervised meta-learning approaches are not scalable for medical image
segmentation, where large effort is required to create pixel-wise annotations.
Meanwhile, in a low data regime, the simulated domain shifts may not
approximate the true domain shifts well across source and unseen domains. To
address this problem, we propose a novel semi-supervised meta-learning
framework with disentanglement. We explicitly model the representations related
to domain shifts. Disentangling the representations and combining them to
reconstruct the input image allows unlabeled data to be used to better
approximate the true domain shifts for meta-learning. Hence, the model can
achieve better generalisation performance, especially when there is a limited
amount of labeled data. Experiments show that the proposed method is robust on
different segmentation tasks and achieves state-of-the-art generalisation
performance on two public benchmarks.Comment: Accepted by MICCAI 202
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. However, the expensive exercise of obtaining pixel-level
annotations for large amounts of data is not always feasible, and performance
is often heavily reduced in a low-annotated data regime. To tackle this
challenge, we adapt a mixed supervision framework, vMFNet, to learn robust
compositional representations using unsupervised learning and weak supervision
alongside non-exhaustive pixel-level pathology labels. In particular, we use
the BraTS dataset to simulate a collection of 2-point expert pathology
annotations indicating the top and bottom slice of the tumour (or tumour
sub-regions: peritumoural edema, GD-enhancing tumour, and the necrotic /
non-enhancing tumour) in each MRI volume, from which weak image-level labels
that indicate the presence or absence of the tumour (or the tumour sub-regions)
in the image are constructed. Then, vMFNet models the encoded image features
with von-Mises-Fisher (vMF) distributions, via learnable and compositional vMF
kernels which capture information about structures in the images. We show that
good tumour segmentation performance can be achieved with a large amount of
weakly labelled data but only a small amount of fully-annotated data.
Interestingly, emergent learning of anatomical structures occurs in the
compositional representation even given only supervision relating to pathology
(tumour).Comment: Accepted by DART workshop, MICCAI 202
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. A good general representation can be fine-tuned for new target
tasks using modest amounts of data, or used directly in unseen domains
achieving remarkable performance in the corresponding task. This alleviation of
the data and annotation requirements offers tantalising prospects for
applications in computer vision and healthcare. In this tutorial paper, we
motivate the need for disentangled representations, present key theory, and
detail practical building blocks and criteria for learning such
representations. We discuss applications in medical imaging and computer vision
emphasising choices made in exemplar key works. We conclude by presenting
remaining challenges and opportunities.Comment: Submitted. This paper follows a tutorial style but also surveys a
considerable (more than 200 citations) number of work
Teacher-Student chain for efficient semi-supervised histology image classification
Deep learning shows great potential for the domain of digital pathology. An
automated digital pathology system could serve as a second reader, perform
initial triage in large screening studies, or assist in reporting. However, it
is expensive to exhaustively annotate large histology image databases, since
medical specialists are a scarce resource. In this paper, we apply the
semi-supervised teacher-student knowledge distillation technique proposed by
Yalniz et al. (2019) to the task of quantifying prognostic features in
colorectal cancer. We obtain accuracy improvements through extending this
approach to a chain of students, where each student's predictions are used to
train the next student i.e. the student becomes the teacher. Using the chain
approach, and only 0.5% labelled data (the remaining 99.5% in the unlabelled
pool), we match the accuracy of training on 100% labelled data. At lower
percentages of labelled data, similar gains in accuracy are seen, allowing some
recovery of accuracy even from a poor initial choice of labelled training set.
In conclusion, this approach shows promise for reducing the annotation burden,
thus increasing the affordability of automated digital pathology systems.Comment: AI for Affordable Healthcare (AI4AH) workshop at ICLR 202
Controllable Chest X-Ray Report Generation from Longitudinal Representations
Radiology reports are detailed text descriptions of the content of medical
scans. Each report describes the presence/absence and location of relevant
clinical findings, commonly including comparison with prior exams of the same
patient to describe how they evolved. Radiology reporting is a time-consuming
process, and scan results are often subject to delays. One strategy to speed up
reporting is to integrate automated reporting systems, however clinical
deployment requires high accuracy and interpretability. Previous approaches to
automated radiology reporting generally do not provide the prior study as
input, precluding comparison which is required for clinical accuracy in some
types of scans, and offer only unreliable methods of interpretability.
Therefore, leveraging an existing visual input format of anatomical tokens, we
introduce two novel aspects: (1) longitudinal representation learning -- we
input the prior scan as an additional input, proposing a method to align,
concatenate and fuse the current and prior visual information into a joint
longitudinal representation which can be provided to the multimodal report
generation model; (2) sentence-anatomy dropout -- a training strategy for
controllability in which the report generator model is trained to predict only
sentences from the original report which correspond to the subset of anatomical
regions given as input. We show through in-depth experiments on the MIMIC-CXR
dataset how the proposed approach achieves state-of-the-art results while
enabling anatomy-wise controllable report generation.Comment: Accepted to the Findings of EMNLP 202
Automated clinical coding using off-the-shelf large language models
The task of assigning diagnostic ICD codes to patient hospital admissions is
typically performed by expert human coders. Efforts towards automated ICD
coding are dominated by supervised deep learning models. However, difficulties
in learning to predict the large number of rare codes remain a barrier to
adoption in clinical practice. In this work, we leverage off-the-shelf
pre-trained generative large language models (LLMs) to develop a practical
solution that is suitable for zero-shot and few-shot code assignment, with no
need for further task-specific training. Unsupervised pre-training alone does
not guarantee precise knowledge of the ICD ontology and specialist clinical
coding task, therefore we frame the task as information extraction, providing a
description of each coded concept and asking the model to retrieve related
mentions. For efficiency, rather than iterating over all codes, we leverage the
hierarchical nature of the ICD ontology to sparsely search for relevant codes.Comment: Accepted to the NeurIPS 2023 workshop Deep Generative Models For
Health (DGM4H). 9 pages, 3 figure
Compositionally Equivariant Representation Learning
Deep learning models often need sufficient supervision (i.e. labelled data)
in order to be trained effectively. By contrast, humans can swiftly learn to
identify important anatomy in medical images like MRI and CT scans, with
minimal guidance. This recognition capability easily generalises to new images
from different medical facilities and to new tasks in different settings. This
rapid and generalisable learning ability is largely due to the compositional
structure of image patterns in the human brain, which are not well represented
in current medical models. In this paper, we study the utilisation of
compositionality in learning more interpretable and generalisable
representations for medical image segmentation. Overall, we propose that the
underlying generative factors that are used to generate the medical images
satisfy compositional equivariance property, where each factor is compositional
(e.g. corresponds to the structures in human anatomy) and also equivariant to
the task. Hence, a good representation that approximates well the ground truth
factor has to be compositionally equivariant. 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. Extensive results show that our methods achieve the best performance
over several strong baselines on the task of semi-supervised domain-generalised
medical image segmentation. Code will be made publicly available upon
acceptance at https://github.com/vios-s.Comment: Submitted. 10 pages. arXiv admin note: text overlap with
arXiv:2206.1453
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