Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

@article{Kermany2018IdentifyingMD,
  title={Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning},
  author={Daniel S. Kermany and Michael H. Goldbaum and Wenjia Cai and Carolina Carvalho Soares Valentim and Huiying Liang and Sally L. Baxter and Alex McKeown and Ge Yang and Xiaokang Wu and Fangbing Yan and Justin Dong and Made K. Prasadha and Jacqueline Pei and Magdalene Yin Lin Ting and Jie Zhu and Christina Li and Sierra Hewett and Jason Dong and Ian Ziyar and Alexander Shi and Runze Zhang and Lianghong Zheng and Rui Hou and William Shi and Xin Fu and Yaou Duan and Viet Anh Nguyen Huu and Cindy Wen and Edward D. Zhang and Charlotte L. Zhang and Oulan Li and Xiaobo Wang and Michael A. Singer and Xiaodong Sun and Jie Xu and Ali R. Tafreshi and M. Anthony Lewis and Huimin Xia and Kang Zhang},
  journal={Cell},
  year={2018},
  volume={172},
  pages={1122-1131.e9}
}
The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. [...] Key Result Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate…Expand
Interpretation of deep learning using attributions: application to ophthalmic diagnosis
TLDR
A framework for interpreting the decision making of a deep learning network for retinal OCT image classification is proposed and results showed a successful attribution of the specific pathological regions of the OCT that are responsible for a given condition in the absence of any pixel-level annotations. Expand
Application of Deep Learning for Diagnosing, Classifying, and Treating Age-Related Macular Degeneration
TLDR
Together with advances in telemedicine and imaging technology, deep learning can enable large populations of patients to be screened than would otherwise be possible and allow ophthalmologists to focus on seeing those patients who are in need of treatment, thus reducing the number of patients with significant visual impairment from AMD. Expand
A transfer learning method with deep residual network for pediatric pneumonia diagnosis
TLDR
A deep learning framework that combines residual thought and dilated convolution to diagnose and detect childhood pneumonia and which can effectively solve the problem of low image resolution and partial occlusion of the inflammatory area in children chest X-ray images. Expand
Machine learning in medical imaging
TLDR
This chapter examines the current field of ML methods and their utility in clinical applications in decision support systems, with particular emphasis on advances from developments in deep learning. Expand
Deep CNN framework for retinal disease diagnosis using optical coherence tomography images
TLDR
A deep convolutional neural network framework for the diagnosis and classification into Normal, DMD and DME effectively is proposed effectively and an authoritative study is performed between the pre-trained models and proposed framework using the acquired performance metrics to demonstrate the efficacy of the model. Expand
A Data-Efficient Approach for Automated Classification of OCT Images Using Generative Adversarial Network
TLDR
A data-efficient semisupervised generative adversarial network based classifier for automated diagnosis with limited labeled data and shows an overall improvement of more than 10% in accuracy, compared to the state-of-the-art methods. Expand
A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
TLDR
A hierarchical deep learning system based on a small number of samples that comprehensively simulates the diagnostic thinking of human experts in diagnosing glaucoma is established and markedly improves the diagnostic accuracy of ophthalmologists. Expand
Artificial intelligence-based decision-making for age-related macular degeneration
TLDR
An AI- and cloud-based telemedicine interaction tool for diagnosis and proposed treatment of AMD is presented and a website for realistic cloud computing based on this AI platform, available at https://www.ym.edu.tw/~AI-OCT/. Expand
Computer-Aided Diagnosis of Ophthalmic Diseases Using OCT Based on Deep Learning: A Review
TLDR
The computer-aided diagnosis system of multiple ophthalmic diseases using OCT in recent years is introduced, including age-related macular degeneration, glaucoma, diabetic macular edema and so on, and an overview of the main challenges faced by deep learning in OCT imaging is introduced. Expand
Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography
TLDR
A deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal diseases is described, which is a customized convolutional neural network for processing scans extracted from optical coherence tomography volumes. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 24 REFERENCES
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration
TLDR
Deep learning techniques achieve high accuracy and is effective as a new image classification technique in Optical coherence tomography and have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future. Expand
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
TLDR
An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes. Expand
Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images
TLDR
An effort at image understanding in fundus images anticipates the future use of medical images by ophthalmologists and physicians in other fields that rely in images. Expand
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
TLDR
An automated method to locate and outline blood vessels in images of the ocular fundus that uses local and global vessel features cooperatively to segment the vessel network is described. Expand
Visualizing and Understanding Convolutional Networks
TLDR
A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. Expand
How transferable are features in deep neural networks?
TLDR
This paper quantifies the generality versus specificity of neurons in each layer of a deep convolutional neural network and reports a few surprising results, including that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset. Expand
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
TLDR
DeCAF, an open-source implementation of deep convolutional activation features, along with all associated network parameters, are released to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms. Expand
The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact [Invited].
TLDR
The translation of OCT from fundamental research to clinical practice and commercial impact is discussed, as well as the ecosystem that helped power OCT to where it is today and will continue to drive future advances is described. Expand
ImageNet classification with deep convolutional neural networks
TLDR
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. Expand
Rethinking the Inception Architecture for Computer Vision
TLDR
This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. Expand
...
1
2
3
...