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 …
methodology of choice for analyzing medical images. This paper reviews the major deep …
Artificial intelligence and machine learning in spine research
F Galbusera, G Casaroli, T Bassani - JOR spine, 2019 - Wiley Online Library
Artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing several
industrial and research fields like computer vision, autonomous driving, natural language …
industrial and research fields like computer vision, autonomous driving, natural language …
Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks
Purpose Intensity modulated radiation therapy (IMRT) is commonly employed for treating
head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing …
head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing …
[HTML][HTML] Survey of deep learning in breast cancer image analysis
TG Debelee, F Schwenker, A Ibenthal, D Yohannes - Evolving Systems, 2020 - Springer
Computer-aided image analysis for better understanding of images has been time-honored
approaches in the medical computing field. In the conventional machine learning approach …
approaches in the medical computing field. In the conventional machine learning approach …
Content-based brain tumor retrieval for MR images using transfer learning
This paper presents an automatic content-based image retrieval (CBIR) system for brain
tumors on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key …
tumors on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key …
Clinical big data and deep learning: Applications, challenges, and future outlooks
The explosion of digital healthcare data has led to a surge of data-driven medical research
based on machine learning. In recent years, as a powerful technique for big data, deep …
based on machine learning. In recent years, as a powerful technique for big data, deep …
[HTML][HTML] Deep semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow algorithm
K Xia, H Yin, Y Zhang - Journal of medical systems, 2019 - Springer
Renal segmentation is one of the most fundamental and challenging task in computer aided
diagnosis systems. In order to overcome the shortcomings of automatic kidney segmentation …
diagnosis systems. In order to overcome the shortcomings of automatic kidney segmentation …
A deep community based approach for large scale content based X-ray image retrieval
A computer assisted system for automatic retrieval of medical images with similar image
contents can serve as an efficient management tool for handling and mining large scale …
contents can serve as an efficient management tool for handling and mining large scale …
Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low‐field MR images
Purpose Image‐guided radiotherapy provides images not only for patient positioning but
also for online adaptive radiotherapy. Accurate delineation of organs‐at‐risk (OAR s) on …
also for online adaptive radiotherapy. Accurate delineation of organs‐at‐risk (OAR s) on …
Learning deep representations of medical images using siamese cnns with application to content-based image retrieval
Deep neural networks have been investigated in learning latent representations of medical
images, yet most of the studies limit their approach in a single supervised convolutional …
images, yet most of the studies limit their approach in a single supervised convolutional …