Identifying and Categorizing Anomalies in Retinal Imaging Data

Optical coherence tomography

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, et al · 1991

Earlier work this paper cites.

On clustering validation techniques

M. Halkidi, Y. Batistakis, and M. Vazirgiannis · 2001

Earlier work this paper cites.

Estimating the support of a high-dimensional distribution

B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson · 2001

Earlier work this paper cites.

Visualizing data using t-sne

L. Van der Maaten and G. Hinton · 2008

Earlier work this paper cites.

Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka · 2009

Earlier work this paper cites.

Spherical k-means clustering

K. Hornik, I. Feinerer, M. Kober, and C. Buchta · 2012

Earlier work this paper cites.

Discriminative unsupervised feature learning with convolutional neural networks

A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014

Earlier work this paper cites.

Over-segmentation of 3d medical image volumes based on monogenic cues

M. Holzer and R. Donner · 2014

Cited alongside, same era.

A review of novelty detection

M. A. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko · 2014

Cited alongside, same era.

Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis

W. L. Wong, X. Su, X. Li, C. M. G. Cheung, R. Klein, C.-Y. Cheng, and T. Y. Wong · 2014

Cited alongside, same era.

Detecting anomalous structures by convolutional sparse models

D. Carrera, G. Boracchi, A. Foi, and B. Wohlberg · 2015

Cited alongside, same era.

Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals

M. Cho, S. Kwak, C. Schmid, and J. Ponce · 2015

Cited alongside, same era.

Fast and accurate deep network learning by exponential linear units (elus)

Faster r-cnn: Towards real-time object detection with region proposal networks

S. Ren, K. He, R. Girshick, and J. Sun · 2015

Later among the works it cites.

Predicting semantic descriptions from medical images with convolutional neural networks

T. Schlegl, S. M. Waldstein, W.-D. Vogl, U. Schmidt-Erfurth, and G. Langs · 2015

Later among the works it cites.

Going deeper with convolutions

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015

Later among the works it cites.

Automated age-related macular degeneration classification in oct using unsupervised feature learning

F. G. Venhuizen, B. van Ginneken, B. Bloemen, M. J. van Grinsven, R. Philipsen, C. Hoyng, T. Theelen, and C. I. Sánchez · 2015

Later among the works it cites.

Stacked what-where auto-encoders

Original

J. Zhao, M. Mathieu, R. Goroshin, and Y. Lecun · 2015

Later among the works it cites.