A Perspective on Deep Imaging
Reads0
Chats0
TLDR
The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction as discussed by the authors, and the latter aspect is considered in this perspective article with an emphasis on medical imaging to develop a new generation of image reconstruction theories and techniques.Abstract:
The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction. The latter aspect is considered in this perspective article with an emphasis on medical imaging to develop a new generation of image reconstruction theories and techniques. This direction might lead to intelligent utilization of domain knowledge from big data, innovative approaches for image reconstruction, and superior performance in clinical and preclinical applications. To realize the full impact of machine learning for tomographic imaging, major theoretical, technical and translational efforts are immediately needed.read more
Citations
More filters
Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: 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, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Artificial intelligence in radiology
Ahmed Hosny,Chintan Parmar,John Quackenbush,Lawrence H. Schwartz,Lawrence H. Schwartz,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +6 more
TL;DR: A general understanding of AI methods, particularly those pertaining to image-based tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology.
Journal ArticleDOI
Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
TL;DR: This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.
Journal ArticleDOI
Learning a variational network for reconstruction of accelerated MRI data.
Kerstin Hammernik,Teresa Klatzer,Erich Kobler,Michael P. Recht,Daniel K. Sodickson,Thomas Pock,Thomas Pock,Florian Knoll +7 more
TL;DR: In this paper, a variational network approach is proposed to reconstruct the clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data.
Journal ArticleDOI
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
Guang Yang,Simiao Yu,Hao Dong,Greg Slabaugh,Pier Luigi Dragotti,Xujiong Ye,Fangde Liu,Simon R. Arridge,Jennifer Keegan,Yike Guo,David N. Firmin +10 more
TL;DR: This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets.
References
More filters
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
The Structure of Scientific Revolutions
TL;DR: The Structure of Scientific Revolutions as discussed by the authors is a seminal work in the history of science and philosophy of science, and it has been widely cited as a major source of inspiration for the present generation of scientists.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.