3,033 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Digital mammography, cancer screening: Factors important for image compression
The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Revisiting model self-interpretability in a decision-theoretic way for binary medical image classification
Interpretability is highly desired for deep neural network-based classifiers,
especially when addressing high-stake decisions in medical imaging. Commonly
used post-hoc interpretability methods have the limitation that they can
produce plausible but different interpretations of a given model, leading to
ambiguity about which one to choose. To address this problem, a novel
decision-theory-motivated approach is investigated to establish a
self-interpretable model, given a pretrained deep binary black-box medical
image classifier. This approach involves utilizing a self-interpretable
encoder-decoder model in conjunction with a single-layer fully connected
network with unity weights. The model is trained to estimate the test statistic
of the given trained black-box deep binary classifier to maintain a similar
accuracy. The decoder output image, referred to as an equivalency map, is an
image that represents a transformed version of the to-be-classified image that,
when processed by the fixed fully connected layer, produces the same test
statistic value as the original classifier. The equivalency map provides a
visualization of the transformed image features that directly contribute to the
test statistic value and, moreover, permits quantification of their relative
contributions. Unlike the traditional post-hoc interpretability methods, the
proposed method is self-interpretable, quantitative, and fundamentally based on
decision theory. Detailed quantitative and qualitative analysis have been
performed with three different medical image binary classification tasks
BMAD: Benchmarks for Medical Anomaly Detection
Anomaly detection (AD) is a fundamental research problem in machine learning
and computer vision, with practical applications in industrial inspection,
video surveillance, and medical diagnosis. In medical imaging, AD is especially
vital for detecting and diagnosing anomalies that may indicate rare diseases or
conditions. However, there is a lack of a universal and fair benchmark for
evaluating AD methods on medical images, which hinders the development of more
generalized and robust AD methods in this specific domain. To bridge this gap,
we introduce a comprehensive evaluation benchmark for assessing anomaly
detection methods on medical images. This benchmark encompasses six reorganized
datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT,
chest X-ray, and digital histopathology) and three key evaluation metrics, and
includes a total of fourteen state-of-the-art AD algorithms. This standardized
and well-curated medical benchmark with the well-structured codebase enables
comprehensive comparisons among recently proposed anomaly detection methods. It
will facilitate the community to conduct a fair comparison and advance the
field of AD on medical imaging. More information on BMAD is available in our
GitHub repository: https://github.com/DorisBao/BMA
Detection of lung pathology using the fractal method
Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis.
Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study
Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer
Invariant Scattering Transform for Medical Imaging
Over the years, the Invariant Scattering Transform (IST) technique has become
popular for medical image analysis, including using wavelet transform
computation using Convolutional Neural Networks (CNN) to capture patterns'
scale and orientation in the input signal. IST aims to be invariant to
transformations that are common in medical images, such as translation,
rotation, scaling, and deformation, used to improve the performance in medical
imaging applications such as segmentation, classification, and registration,
which can be integrated into machine learning algorithms for disease detection,
diagnosis, and treatment planning. Additionally, combining IST with deep
learning approaches has the potential to leverage their strengths and enhance
medical image analysis outcomes. This study provides an overview of IST in
medical imaging by considering the types of IST, their application,
limitations, and potential scopes for future researchers and practitioners
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