Introduction to radiomics
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative
metrics—the so-called radiomic features—within medical images. Radiomic features capture …
metrics—the so-called radiomic features—within medical images. Radiomic features capture …
A survey on image data augmentation for deep learning
C Shorten, TM Khoshgoftaar - Journal of big data, 2019 - Springer
Deep convolutional neural networks have performed remarkably well on many Computer
Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting …
Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting …
Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning
Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is
becoming an attractive solution in medical image segmentation. To make use of unlabeled …
becoming an attractive solution in medical image segmentation. To make use of unlabeled …
A review on medical imaging synthesis using deep learning and its clinical applications
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …
clinical application. Specifically, we summarized the recent developments of deep learning …
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …
including image acquisition, analysis and interpretation, and for the extraction of clinically …
Generative adversarial network in medical imaging: A review
Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …
community due to their capability of data generation without explicitly modelling the …
Medical Image Segmentation based on U-Net: A Review.
Medical image analysis is performed by analyzing images obtained by medical imaging
systems to solve clinical problems. The purpose is to extract effective information and …
systems to solve clinical problems. The purpose is to extract effective information and …
[HTML][HTML] The promise of artificial intelligence and deep learning in PET and SPECT imaging
This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
Cancer diagnosis using deep learning: a bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …
steps of cancer diagnosis followed by the typical classification methods used by doctors …
Machine learning and deep learning in medical imaging: intelligent imaging
Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An
understanding of the principles and application of radiomics, artificial neural networks …
understanding of the principles and application of radiomics, artificial neural networks …