18 research outputs found
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review
The COVID-19 pandemic has forced many people to limit their social
activities, which has resulted in a rise in mental illnesses, particularly
depression. To diagnose these illnesses with accuracy and speed, and prevent
severe outcomes such as suicide, the use of machine learning has become
increasingly important. Additionally, to provide precise and understandable
diagnoses for better treatment, AI scientists and researchers must develop
interpretable AI-based solutions. This article provides an overview of relevant
articles in the field of machine learning and interpretable AI, which helps to
understand the advantages and disadvantages of using AI in psychiatry disorder
detection applications.Comment: 12 page
Automatic Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment using Unsupervised Domain Adaptation in Spatial and Frequency Domains
Population imaging studies rely upon good quality medical imagery before
downstream image quantification. This study provides an automated approach to
assess image quality from cardiovascular magnetic resonance (CMR) imaging at
scale. We identify four common CMR imaging artefacts, including respiratory
motion, cardiac motion, Gibbs ringing, and aliasing. The model can deal with
images acquired in different views, including two, three, and four-chamber
long-axis and short-axis cine CMR images. Two deep learning-based models in
spatial and frequency domains are proposed. Besides recognising these
artefacts, the proposed models are suitable to the common challenges of not
having access to data labels. An unsupervised domain adaptation method and a
Fourier-based convolutional neural network are proposed to overcome these
challenges. We show that the proposed models reliably allow for CMR image
quality assessment. The accuracies obtained for the spatial model in supervised
and weakly supervised learning are 99.41+0.24 and 96.37+0.66 for the UK Biobank
dataset, respectively. Using unsupervised domain adaptation can somewhat
overcome the challenge of not having access to the data labels. The maximum
achieved domain gap coverage in unsupervised domain adaptation is 16.86%.
Domain adaptation can significantly improve a 5-class classification task and
deal with considerable domain shift without data labels. Increasing the speed
of training and testing can be achieved with the proposed model in the
frequency domain. The frequency-domain model can achieve the same accuracy yet
1.548 times faster than the spatial model. This model can also be used directly
on k-space data, and there is no need for image reconstruction.Comment: 21 pages, 9 figures, 7 table
A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images
Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is
a powerful modality in functional and anatomical assessment for various
cardiovascular diseases. Sufficient image quality is essential to achieve
proper diagnosis and treatment. A large number of medical images, the variety
of imaging artefacts, and the workload of imaging centres are among the things
that reveal the necessity of automatic image quality assessment (IQA). However,
automated IQA requires access to bulk annotated datasets for training deep
learning (DL) models. Labelling medical images is a tedious, costly and
time-consuming process, which creates a fundamental challenge in proposing
DL-based methods for medical applications. This study aims to present a new
method for CMR IQA when there is limited access to annotated datasets. Methods:
The proposed generalised deep meta-learning model can evaluate the quality by
learning tasks in the prior stage and then fine-tuning the resulting model on a
small labelled dataset of the desired tasks. This model was evaluated on the
data of over 6,000 subjects from the UK Biobank for five defined tasks,
including detecting respiratory motion, cardiac motion, Aliasing and Gibbs
ringing artefacts and images without artefacts. Results: The results of
extensive experiments show the superiority of the proposed model. Besides,
comparing the model's accuracy with the domain adaptation model indicates a
significant difference by using only 64 annotated images related to the desired
tasks. Conclusion: The proposed model can identify unknown artefacts in images
with acceptable accuracy, which makes it suitable for medical applications and
quality assessment of large cohorts.Comment: 16 pages, 1 figure, 2 table