94 research outputs found
Artificial Intelligence Techniques in Medical Imaging: A Systematic Review
This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases
Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
Pneumothorax is a medical emergency caused by abnormal accumulation of air in
the pleural space - the potential space between the lungs and chest wall. On 2D
chest radiographs, pneumothorax occurs within the thoracic cavity and outside
of the mediastinum and we refer to this area as "lung+ space". While deep
learning (DL) has increasingly been utilized to segment pneumothorax lesions in
chest radiographs, many existing DL models employ an end-to-end approach. These
models directly map chest radiographs to clinician-annotated lesion areas,
often neglecting the vital domain knowledge that pneumothorax is inherently
location-sensitive.
We propose a novel approach that incorporates the lung+ space as a constraint
during DL model training for pneumothorax segmentation on 2D chest radiographs.
To circumvent the need for additional annotations and to prevent potential
label leakage on the target task, our method utilizes external datasets and an
auxiliary task of lung segmentation. This approach generates a specific
constraint of lung+ space for each chest radiograph. Furthermore, we have
incorporated a discriminator to eliminate unreliable constraints caused by the
domain shift between the auxiliary and target datasets.
Our results demonstrated significant improvements, with average performance
gains of 4.6%, 3.6%, and 3.3% regarding Intersection over Union (IoU), Dice
Similarity Coefficient (DSC), and Hausdorff Distance (HD). Our research
underscores the significance of incorporating medical domain knowledge about
the location-specific nature of pneumothorax to enhance DL-based lesion
segmentation
Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images
Since 2019, the global dissemination of the Coronavirus and its novel strains
has resulted in a surge of new infections. The use of X-ray and computed
tomography (CT) imaging techniques is critical in diagnosing and managing
COVID-19. Incorporating artificial intelligence (AI) into the field of medical
imaging is a powerful combination that can provide valuable support to
healthcare professionals.This paper focuses on the methodological approach of
using machine learning (ML) to enhance medical imaging for COVID-19
diagnosis.For example, deep learning can accurately distinguish lesions from
other parts of the lung without human intervention in a matter of
minutes.Moreover, ML can enhance performance efficiency by assisting
radiologists in making more precise clinical decisions, such as detecting and
distinguishing Covid-19 from different respiratory infections and segmenting
infections in CT and X-ray images, even when the lesions have varying sizes and
shapes.This article critically assesses machine learning methodologies utilized
for the segmentation, classification, and detection of Covid-19 within CT and
X-ray images, which are commonly employed tools in clinical and hospital
settings to represent the lung in various aspects and extensive detail.There is
a widespread expectation that this technology will continue to hold a central
position within the healthcare sector, driving further progress in the
management of the pandemic
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