Fetching the paper…
Reading the bibliography…
The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas.
Pathological prognostic factors in breast cancer. i. the value of histological grade in breast cancer: experience from a large study with long-term follow-up
C. W. Elston, I. O. Ellis, et al · 1991
Earlier work this paper cites.
Histologic grading of prostate cancer: a perspective
D. F. Gleason · 1992
Earlier work this paper cites.
The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia
J. Diamond, N. H. Anderson, P. H. Bartels, R. Montironi, and P. W. Hamilton · 2004
Earlier work this paper cites.
Segmentation of intestinal gland images with iterative region growing
H.-S. WU, R. Xu, N. Harpaz, D. Burstein, and J. Gil · 2005
Earlier work this paper cites.
A boosting cascade for automated detection of prostate cancer from digitized histology
S. Doyle, A. Madabhushi, M. Feldman, and J. Tomaszeweski · 2006
Earlier work this paper cites.
Multifeature prostate cancer diagnosis and gleason grading of histological images
A. Tabesh, M. Teverovskiy, H.-Y. Pang, V. P. Kumar, D. Verbel, A. Kotsianti, and O. Saidi · 2007
Earlier work this paper cites.
Color graphs for automated cancer diagnosis and grading
D. Altunbay, C. Cigir, C. Sokmensuer, and C. Gunduz-Demir · 2010
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Automatic segmentation of colon glands using object-graphs
C. Gunduz-Demir, M. Kandemir, A. B. Tosun, and C. Sokmensuer · 2010
Earlier work this paper cites.
Automated analysis of pin-4 stained prostate needle biopsies
B. Sabata, B. Babenko, R. Monroe, and C. Srinivas · 2010
Earlier work this paper cites.
Deep neural networks segment neuronal membranes in electron microscopy images
D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber · 2012
Earlier work this paper cites.
Analyzing tubular tissue in histopathological thin sections
A. Fakhrzadeh, E. Sporndly-Nees, L. Holm, and C. L. L. Hendriks · 2012
Earlier work this paper cites.
Colorectal carcinoma: pathologic aspects
M. Fleming, S. Ravula, S. F. Tatishchev, and H. L. Wang · 2012
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Structure and context in prostatic gland segmentation and classification
K. Nguyen, A. Sarkar, and A. K. Jain · 2012
Cited alongside, same era.
A novel polar space random field model for the detection of glandular structures
H. Fu, G. Qiu, J. Shu, and M. Ilyas · 2014
Cited alongside, same era.
Gleason grading of prostate tumours with max-margin conditional random fields
J. G. Jacobs, E. Panagiotaki, and D. C. Alexander · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Later among the works it cites.
Deeply-supervised nets
C. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
Later among the works it cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Later among the works it cites.
Semantic segmentation with object clique potential
X. Qi, J. Shi, S. Liu, R. Liao, and J. Jia · 2015
Later among the works it cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Later among the works it cites.
Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation
H. R. Roth, L. Lu, A. Farag, H.-C. Shin, J. Liu, E. B. Turkbey, and R. M. Summers · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Cited alongside, same era.
Facial landmark detection by deep multi-task learning
Z. Zhang, P. Luo, C. C. Loy, and X. Tang · 2014
Cited alongside, same era.
High-for-low and low-for-high: Efficient boundary detection from deep object features and its applications to high-level vision
G. Bertasius, J. Shi, and L. Torresani · 2015
Cited alongside, same era.
Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks
H. Chen, Q. Dou, D. Ni, J.-Z. Cheng, J. Qin, S. Li, and P.-A. Heng · 2015
Cited alongside, same era.
Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks
H. Chen, C. Shen, J. Qin, D. Ni, L. Shi, J. C. Cheng, and P.-A. Heng · 2015
Cited alongside, same era.
Semantic image segmentation with deep convolutional nets and fully connected CRFs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Cited alongside, same era.
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
Cited alongside, same era.
Later among the works it cites.
A stochastic polygons model for glandular structures in colon histology images
K. Sirinukunwattana, D. Snead, and N. Rajpoot · 2015
Later among the works it cites.
A novel texture descriptor for detection of glandular structures in colon histology images
K. Sirinukunwattana, D. R. Snead, and N. M. Rajpoot · 2015
Later among the works it cites.
Holistically-nested edge detection
S. Xie and Z. Tu · 2015
Later among the works it cites.
Deep contextual networks for neuronal structure segmentation
H. Chen, X. Qi, J.-Z. Cheng, and P.-A. Heng · 2016
Closest in time.
Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks
Q. Dou, H. Chen, Y. Lequan, L. Zhao, J. Qin, W. Defeng, M. Vincent, L. Shi, and P. A. Heng · 2016
Closest in time.
Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning
H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers · 2016
Closest in time.
Gland segmentation in colon histology images: The GlaS Challenge Contest
K. Sirinukunwattana, J. P. Pluim, H. Chen, X. Qi, P.-A. Heng, Y. B. Guo, L. Y. Wang, B. J. Matuszewski, E. Bruni, U. Sanchez, et al · 2016
Closest in time.