874 research outputs found
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
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
Recent Advances in Machine Learning Applied to Ultrasound Imaging
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland
Recent Progress in Transformer-based Medical Image Analysis
The transformer is primarily used in the field of natural language
processing. Recently, it has been adopted and shows promise in the computer
vision (CV) field. Medical image analysis (MIA), as a critical branch of CV,
also greatly benefits from this state-of-the-art technique. In this review, we
first recap the core component of the transformer, the attention mechanism, and
the detailed structures of the transformer. After that, we depict the recent
progress of the transformer in the field of MIA. We organize the applications
in a sequence of different tasks, including classification, segmentation,
captioning, registration, detection, enhancement, localization, and synthesis.
The mainstream classification and segmentation tasks are further divided into
eleven medical image modalities. A large number of experiments studied in this
review illustrate that the transformer-based method outperforms existing
methods through comparisons with multiple evaluation metrics. Finally, we
discuss the open challenges and future opportunities in this field. This
task-modality review with the latest contents, detailed information, and
comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte
Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT
In this paper, we aimed to provide a review and tutorial for researchers in
the field of medical imaging using language models to improve their tasks at
hand. We began by providing an overview of the history and concepts of language
models, with a special focus on large language models. We then reviewed the
current literature on how language models are being used to improve medical
imaging, emphasizing different applications such as image captioning, report
generation, report classification, finding extraction, visual question
answering, interpretable diagnosis, and more for various modalities and organs.
The ChatGPT was specially highlighted for researchers to explore more potential
applications. We covered the potential benefits of accurate and efficient
language models for medical imaging analysis, including improving clinical
workflow efficiency, reducing diagnostic errors, and assisting healthcare
professionals in providing timely and accurate diagnoses. Overall, our goal was
to bridge the gap between language models and medical imaging and inspire new
ideas and innovations in this exciting area of research. We hope that this
review paper will serve as a useful resource for researchers in this field and
encourage further exploration of the possibilities of language models in
medical imaging
Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review
Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Cancer remains one of the most challenging diseases to treat in the medical
field. Machine learning has enabled in-depth analysis of rich multi-omics
profiles and medical imaging for cancer diagnosis and prognosis. Despite these
advancements, machine learning models face challenges stemming from limited
labeled sample sizes, the intricate interplay of high-dimensionality data
types, the inherent heterogeneity observed among patients and within tumors,
and concerns about interpretability and consistency with existing biomedical
knowledge. One approach to surmount these challenges is to integrate biomedical
knowledge into data-driven models, which has proven potential to improve the
accuracy, robustness, and interpretability of model results. Here, we review
the state-of-the-art machine learning studies that adopted the fusion of
biomedical knowledge and data, termed knowledge-informed machine learning, for
cancer diagnosis and prognosis. Emphasizing the properties inherent in four
primary data types including clinical, imaging, molecular, and treatment data,
we highlight modeling considerations relevant to these contexts. We provide an
overview of diverse forms of knowledge representation and current strategies of
knowledge integration into machine learning pipelines with concrete examples.
We conclude the review article by discussing future directions to advance
cancer research through knowledge-informed machine learning.Comment: 41 pages, 4 figures, 2 table
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