A unified representation network for segmentation with missing modalities

Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE transactions on medical imaging 29

2010

Earlier work this paper cites.

Ngiam, J., et al.: Multimodal deep learning. In: ICML. pp. 689–696 (2011)

2011

Earlier work this paper cites.

Nelms, B.E., et al.: Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer. IJROBP 82

2012

Earlier work this paper cites.

Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80

2013

Earlier work this paper cites.

Kiros, R., Salakhutdinov, R., Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models. arXiv:1411.2539 (2014)

Original

2014

Earlier work this paper cites.

Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging 34

2015

Earlier work this paper cites.

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI. pp. 234–241. Springer (2015)

2015

Cited alongside, same era.

Havaei, M., et al.: HeMIS: Hetero-modal image segmentation. In: MICCAI. pp. 469–477. Springer (2016)

2016

Cited alongside, same era.

Neverova, N., et al.: ModDrop: adaptive multi-modal gesture recognition. IEEE TPAMI 38

2016

Cited alongside, same era.

Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific data 4

2017

Cited alongside, same era.

Chartsias, A., et al.: Multimodal MR synthesis via modality-invariant latent representation. IEEE transactions on medical imaging (2017)

2017

Cited alongside, same era.

Litjens, G., et al.: A survey on deep learning in medical image analysis. Medical image analysis 42

2017

Later among the works it cites.

Zhang, Z., Yang, L., Zheng, Y.: Translating and segmenting multimodal medical volumes with cycle- and shape-consistency generative adversarial network. In: CVPR. pp. 9242–9251 (2018)

Original

2018

Later among the works it cites.

Ardizzone, L., et al.: Analyzing inverse problems with invertible neural networks. ICLR (2019)

2019

Closest in time.

Radford, A., et al.: Language models are unsupervised multitask learners. Available online (2019)

2019

Closest in time.

van Tulder, G., de Bruijne, M.: Learning cross-modality representations from multi-modal images. IEEE transactions on medical imaging 38

2019

Closest in time.