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.
read more
About: This article is published in Medical Image Analysis. The article was published on 01 Dec 2017. and is currently open access. The article focuses on the topics: Deep learning.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Deep learning in histopathology: the path to the clinic
TL;DR: In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading, but despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques as discussed by the authors.
575
Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications
Tiago Carneiro,Raul Victor Medeiros da Nóbrega,Thiago Nepomuceno,Gui-Bin Bian,Victor Hugo C. de Albuquerque,Pedro Pedrosa Rebouças Filho +5 more
TL;DR: This paper presents a detailed analysis of Colaboratory regarding hardware resources, performance, and limitations and shows that the performance reached using this cloud service is equivalent to the performance of the dedicated testbeds, given similar resources.
573
Imbalance Problems in Object Detection: A Review
TL;DR: A comprehensive review of the imbalance problems in object detection is presented in this article, where the authors introduce a problem-based taxonomy and discuss each problem in depth and present a unifying yet critical perspective on the solutions in the literature.
•Posted Content
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.
TL;DR: An overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis is presented in this paper, where a framework of XAI criteria is introduced to classify deep learning based methods.
569
Deep learning in medical image registration: a survey
Grant Haskins,Uwe Kruger,Pingkun Yan +2 more
- 01 Feb 2020
TL;DR: This survey outlines the evolution of deep learning-based medical image registration in the context of both research challenges and relevant innovations in the past few years and highlights future research directions to show how this field may be possibly moved forward to the next level.
566
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
•Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
117.9K
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
99K
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Related Papers (5)
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
[...]
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014