3 research outputs found
Active Learning on Medical Image
The development of medical science greatly depends on the increased
utilization of machine learning algorithms. By incorporating machine learning,
the medical imaging field can significantly improve in terms of the speed and
accuracy of the diagnostic process. Computed tomography (CT), magnetic
resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron
emission tomography (PET) are the most commonly used types of imaging data in
the diagnosis process, and machine learning can aid in detecting diseases at an
early stage. However, training machine learning models with limited annotated
medical image data poses a challenge. The majority of medical image datasets
have limited data, which can impede the pattern-learning process of
machine-learning algorithms. Additionally, the lack of labeled data is another
critical issue for machine learning. In this context, active learning
techniques can be employed to address the challenge of limited annotated
medical image data. Active learning involves iteratively selecting the most
informative samples from a large pool of unlabeled data for annotation by
experts. By actively selecting the most relevant and informative samples,
active learning reduces the reliance on large amounts of labeled data and
maximizes the model's learning capacity with minimal human labeling effort. By
incorporating active learning into the training process, medical imaging
machine learning models can make more efficient use of the available labeled
data, improving their accuracy and performance. This approach allows medical
professionals to focus their efforts on annotating the most critical cases,
while the machine learning model actively learns from these annotated samples
to improve its diagnostic capabilities.Comment: 12 pages, 8 figures; Acceptance of the chapter for the Springer book
"Data-driven approaches to medical imaging
Introduction of Medical Imaging Modalities
The diagnosis and treatment of various diseases had been expedited with the
help of medical imaging. Different medical imaging modalities, including X-ray,
Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging,
Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies
for in vivo imaging modalities is presented in this chapter, in addition to
these modalities, some advanced techniques such as contrast-enhanced MRI, MR
approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data
mining and search. Despite its important role and potential effectiveness as a
diagnostic tool, reading and interpreting medical images by radiologists is
often tedious and difficult due to the large heterogeneity of diseases and the
limitation of image quality or resolution. Besides the introduction and
discussion of the basic principles, typical clinical applications, advantages,
and limitations of each modality used in current clinical practice, this
chapter also highlights the importance of emerging technologies in medical
imaging and the role of data mining and search aiming to support translational
clinical research, improve patient care, and increase the efficiency of the
healthcare system.Comment: 19 pages, 7 figures, 1 table; Acceptance of the chapter for the
Springer book "Data-driven approaches to medical imaging
AutoML Systems For Medical Imaging
The integration of machine learning in medical image analysis can greatly
enhance the quality of healthcare provided by physicians. The combination of
human expertise and computerized systems can result in improved diagnostic
accuracy. An automated machine learning approach simplifies the creation of
custom image recognition models by utilizing neural architecture search and
transfer learning techniques. Medical imaging techniques are used to
non-invasively create images of internal organs and body parts for diagnostic
and procedural purposes. This article aims to highlight the potential
applications, strategies, and techniques of AutoML in medical imaging through
theoretical and empirical evidence.Comment: 11 pages, 4 figures; Acceptance of the chapter for the Springer book
"Data-driven approaches to medical imaging