SpineNet: Automatically pinpointing classification evidence in spinal MRIs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9901 LNCS, Issue , 2016, Pages 166-175

  Jamaludin, Amir ^a   Kadir, Timor ^b   Zisserman, Andrew ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b Mirada Medical Ltd   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; GRADING; MAGNETIC LEVITATION VEHICLES; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; NEURAL NETWORKS;

CLASS IMBALANCE; CLASS LABELS; CONVOLUTIONAL NEURAL NETWORK; HUMAN PERFORMANCE; MEDICAL CLASSIFICATION; SPINAL MRIS; STATE OF THE ART; TRAINING SCHEMES;

MEDICAL IMAGING;

EID: 84996549471     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46723-8_20     Document Type: Conference Paper

References (14)

1. Bar,Y.,Diamant,I.,Wolf,L.,Lieberman,S.,Konen,E.,Greenspan,H.: Chest pathology detection using deep learning with non-medical training. In: ISBI (2015)
2. Chatfield,K.,Simonyan,K.,Vedaldi,A.,Zisserman,A.: Return of the devil in the details: delving deep into convolutional nets. In: Proceedings of BMVC (2014)
3. Ghosh,S.,Alomari,R.S.,Chaudhary,V.,Dhillon,G.: Computer-aided diagnosis for lumbar mri using heterogeneous classifiers. In: ISBI (2011)
4. Glorot,X.,Bengio,Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of AISTATS (2010)
5. Jamaludin,A.,Kadir,T.,Zisserman,A.: Automatic modic changes classication in spinal mri. In: MICCAI Workshop: CSI (2015)
6. Krizhevsky,A.,Sutskever,I.,Hinton,G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)
7. Lootus,M.: Automated radiological analysis of Spinal MRI. Ph.D. thesis,University of Oxford (2015)
8. Oquab,M.,Bottou,L.,Laptev,I.,Sivic,J.: Is object localization for free? weaklysupervised learning with convolutional neural networks. In: Proceedings of CVPR (2015)
9. Roberts,M.G.,Pacheco,E.M.,Mohankumar,R.,Cootes,T.F.,Adams,J.E.: Detection of vertebral fractures in DXA VFA images using statistical models of appearance and a semi-automatic segmentation. Osteoporos Int. 21(12),2037–2046 (2010)
10. Ronneberger,O.,Fischer,P.,Brox,T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab,N.,Hornegger,J.,Wells,W.M.,Frangi,A.F. (eds.) MICCAI 2015. LNCS,vol. 9351,pp. 234–241. Springer,Heidelberg (2015). doi:10.1007/978-3-319-24574-4_28
11. Roth,H.R.,Yao,J.,Lu,L.,Stieger,J.,Burns,J.E.,Summers,R.M.: Detection of sclerotic spine metastases viarandom aggregation of deep convolutionalneural network classifications. In: MICCAI Workshop: CSI (2014)
12. Shin,H.C.,Roth,H.R.,Gao,M.,Lu,L.,Xu,Z.,Nogues,I.,Yao,J.,Mollura,D.,Summers,R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures,dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5),1285–1298 (2016)
13. Simonyan,K.,Vedaldi,A.,Zisserman,A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: Workshop at International Conference on Learning Representations (2014)
14. Vedaldi,A.,Lenc,K.: MatConvNet - convolutional neural networks for MATLAB. CoRR abs/1412.4564 (2014)