372 research outputs found
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 and Quantum-computing Based Optimization in Medical Imaging and Power Dispatcing
兵庫県立大学大学院202
Automatic dental caries detection in bitewing radiographs.
Doctoral Degree. University of KwaZulu-Natal, Durban.Dental Caries is one of the most prevalent chronic disease around the globe. Distinguishing carious lesions has been a challenging task. Conventional computer aided
diagnosis and detection methods in the past have heavily relied on visual inspection
of teeth. These are only effective on large and clearly visible caries on affected teeth.
Conventional methods have been limited in performance due to the complex visual
characteristics of dental caries images, which consists of hidden or inaccessible lesions.
Early detection of dental caries is an important determinant for treatment and benefits
much from the introduction of new tools such as dental radiography. A method for
the segmentation of teeth in bitewing X-rays is presented in this thesis, as well as a
method for the detection of dental caries on X-ray images using a supervised model.
The diagnostic method proposed uses an assessment protocol that is evaluated according to a set of identifiers obtained from a learning model. The proposed technique
automatically detects hidden and inaccessible dental caries lesions in bitewing radio graphs. The approach employed data augmentation to increase the number of images
in the data set in order to have a total of 11,114 dental images. Image pre-processing
on the data set was through the use of Gaussian blur filters. Image segmentation was
handled through thresholding, erosion and dilation morphology, while image boundary detection was achieved through active contours method. Furthermore, the deep
learning based network through the sequential model in Keras extracts features from
the images through blob detection. Finally, a convexity threshold value of 0.9 is introduced to aid in the classification of caries as either present or not present. The relative
efficacy of the supervised model in diagnosing dental caries when compared to current
systems is indicated by the results detailed in this thesis. The proposed model achieved
a 97% correct diagnostic which proved quite competitive with existing models.Author's Publications are listed on page 4 of this thesis
Advances in Image Processing, Analysis and Recognition Technology
For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches
Artificial Intelligence in Oral Health
This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others
Machine Learning for Biomedical Application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images
Texture and Colour in Image Analysis
Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews
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