368 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
Automated brain tumour identification using magnetic resonance imaging:a systematic review and meta-analysis
BACKGROUND: Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI. METHODS: A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies. RESULTS: Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detection, DL achieved a lower false positive rate compared to TML; 0.018 (95% CI, 0.011 to 0.028) and 0.048 (0.032 to 0.072) (P < .001), respectively. In segmentation, DL had a higher dice similarity coefficient (DSC), particularly for tumor core (TC); 0.80 (0.77 to 0.83) and 0.63 (0.56 to 0.71) (P < .001), persisting on sensitivity analysis. Both manual and automated whole tumor (WT) segmentation had “good” (DSC ≥ 0.70) performance. Manual TC segmentation was superior to automated; 0.78 (0.69 to 0.86) and 0.64 (0.53 to 0.74) (P = .014), respectively. Only 30% of studies reported external validation. CONCLUSIONS: The comparable performance of automated to manual WT segmentation supports its integration into clinical practice. However, manual outperformance for sub-compartmental segmentation highlights the need for further development of automated methods in this area. Compared to TML, DL provided superior performance for detection and sub-compartmental segmentation. Improvements in the quality and design of studies, including external validation, are required for the interpretability and generalizability of automated models
A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis
Over the past 20 years, the global research going on in Artificial Intelligence in applica-tions in medication is a venue internationally, for medical trade and creating an ener-getic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxono-my of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain
Investigating the role of machine learning and deep learning techniques in medical image segmentation
openThis work originates from the growing interest of the medical imaging community in the application of
machine learning techniques and, from deep learning to improve the accuracy of cancerscreening. The thesis
is structured into two different tasks.
In the first part, magnetic resonance images were analysed in order to support clinical experts in the
treatment of patients with brain tumour metastases (BM). The main topic related to this study was to
investigate whether BM segmentation may be approached successfully by two supervised ML classifiers
belonging to feature-based and deep learning approaches, respectively. SVM and V-Net Convolutional Neural
Network model are selected from the literature as representative of the two approaches.
The second task related to this thesisis illustrated the development of a deep learning study aimed to process
and classify lesions in mammograms with the use of slender neural networks. Mammography has a central
role in screening and diagnosis of breast lesions. Deep Convolutional Neural Networks have shown a great
potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and
reproducibility. A traditional convolution network was compared with a novel one obtained making use of
much more efficient depth wise separable convolution layers.
As a final goal to integrate the system developed in clinical practice, for both fields studied, all the Medical
Imaging and Pattern Recognition algorithmic solutions have been integrated into a MATLAB® software
packageopenInformatica e matematica del calcologonella gloriaGonella, Glori
Deep learning for brain metastasis detection and segmentation in longitudinal MRI data
Brain metastases occur frequently in patients with metastatic cancer. Early
and accurate detection of brain metastases is very essential for treatment
planning and prognosis in radiation therapy. To improve brain metastasis
detection performance with deep learning, a custom detection loss called
volume-level sensitivity-specificity (VSS) is proposed, which rates individual
metastasis detection sensitivity and specificity in (sub-)volume levels. As
sensitivity and precision are always a trade-off in a metastasis level, either
a high sensitivity or a high precision can be achieved by adjusting the weights
in the VSS loss without decline in dice score coefficient for segmented
metastases. To reduce metastasis-like structures being detected as false
positive metastases, a temporal prior volume is proposed as an additional input
of DeepMedic. The modified network is called DeepMedic+ for distinction. Our
proposed VSS loss improves the sensitivity of brain metastasis detection for
DeepMedic, increasing the sensitivity from 85.3% to 97.5%. Alternatively, it
improves the precision from 69.1% to 98.7%. Comparing DeepMedic+ with DeepMedic
with the same VSS loss, 44.4% of the false positive metastases are reduced in
the high sensitivity model and the precision reaches 99.6% for the high
specificity model. The mean dice coefficient for all metastases is about 0.81.
With the ensemble of the high sensitivity and high specificity models, on
average only 1.5 false positive metastases per patient needs further check,
while the majority of true positive metastases are confirmed. The ensemble
learning is able to distinguish high confidence true positive metastases from
metastases candidates that require special expert review or further follow-up,
being particularly well-fit to the requirements of expert support in real
clinical practice.Comment: Implementation is available to public at
https://github.com/YixingHuang/DeepMedicPlu
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