Adversarial Deep Structured Nets for Mass Segmentation from Mammograms

“Current status of the digital database for screening mammography,”

M. Heath et al., · 1998

Earlier work this paper cites.

“An example-based system to support the segmentation of stellate lesions,”

M. Beller et al., · 2005

Earlier work this paper cites.

“Digital mammographic computer aided diagnosis using adaptive level set segmentation,”

J. Ball et al., · 2007

Earlier work this paper cites.

“Robust higher order potentials for enforcing label consistency,”

Original

P. Kohli et al., · 2009

Earlier work this paper cites.

“Efficient inference in fully connected crfs with gaussian edge potentials,”

P. Krähenbühl and V. Koltun, · 2011

Earlier work this paper cites.

“Inbreast: toward a full-field digital mammographic database,”

I. C Moreira et al., · 2012

Earlier work this paper cites.

“Intriguing properties of neural networks,”

C. Szegedy et al., · 2014

Cited alongside, same era.

“Closed shortest path in the original coordinates with an application to breast cancer,”

J. S Cardoso et al., · 2015

Cited alongside, same era.

“Deep structured learning for mass segmentation from mammograms,”

N. Dhungel et al., · 2015

Cited alongside, same era.

“Tree re-weighted belief propagation using deep learning potentials for mass segmentation from mammograms,”

N. Dhungel et al., · 2015

Cited alongside, same era.

“Deep learning and structured prediction for the segmentation of mass in mammograms,”

N. Dhungel et al., · 2015

Cited alongside, same era.

“Fully convolutional networks for semantic segmentation,”

J. Long, E. Shelhamer, and T. Darrell, · 2015

Later among the works it cites.

“Conditional random fields as recurrent neural networks,”

S. Zheng et al., · 2015

Later among the works it cites.

“Mammographic mass segmentation with online learned shape and appearance priors,”

M. Jiang et al., · 2016

Later among the works it cites.

“Deep multi-instance networks with sparse label assignment for whole mammogram classification,”

W. Zhu et al, · 2017

Closest in time.

“Automatic liver segmentation using an adversarial image-to-image network,”

D. Yang et al., · 2017

Closest in time.