Fetching the paper…
Reading the bibliography…
Automated computer-aided detection (CADe) in medical imaging has been an important tool in clinical practice and research.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation
1989
Earlier work this paper cites.
S. B. Göktürk, C. Tomasi, B. Acar, C. F. Beaulieu, D. S. Paik, R. B. Jeffrey, J. Yee, and Y. Napel, “A statistical 3-d pattern processing method for computer-aided detection of polyps in CT colonography,” IEEE Trans. on Med. Imag
2001
Earlier work this paper cites.
R. M. Summers, A. K. Jerebko, M. Franaszek, J. D. Malley, and C. D. Johnson, “Colonic polyps: Complementary role of computer-aided detection in CT colonography,” Radiology
2002
Earlier work this paper cites.
H.-D. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou, “Computer-aided detection and classification of microcalcifications in mammograms: a survey,” Pattern recognition
2003
Earlier work this paper cites.
R. M. Summers, J. Yao, P. J. Pickhardt, M. Franaszek, I. Bitter, D. Brickman, V. Krishna, and J. R. Choi, “Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population,” Gastroenterology
2005
Earlier work this paper cites.
J. Yao, R. M. Summers, and A. K. Hara, “Optimizing the support vector machines (svm) committee configuration in a colonic polyp cad system,” in Medical Imaging
2005
Earlier work this paper cites.
J. Yao, S. D. O’Connor, and R. M. Summers, “Automated spinal column extraction and partitioning,” in Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on
2006
Earlier work this paper cites.
J. Yao, S. D. O’Connor, and R. Summers, “Computer aided lytic bone metastasis detection using regular CT images,” in Medical Imaging
2006
Earlier work this paper cites.
A. Barbu, L. Bogoni, and D. Comaniciu, “Hierarchical part-based detection of 3d flexible tubes: Application to ct colonoscopy,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI
2006
Earlier work this paper cites.
M. Toews and T. Arbel, “A statistical parts-based model of anatomical variability,” Medical Imaging, IEEE Transactions on
2007
Earlier work this paper cites.
P. Msaouel, N. Pissimissis, A. Halapas, and M. Koutsilieris, “Mechanisms of bone metastasis in prostate cancer: clinical implications,” Best Practice & Research Clinical Endocrinology & Metabolism
2008
Earlier work this paper cites.
L. Lu, A. Barbu, M. Wolf, J. Liang, L. Bogoni, M. Salganicoff, and D. Comaniciu, “Simultaneous detection and registration for ileo-cecal valve detection in 3d ct colonography,” in Proc. of European Conf. on Computer Vision
2008
Earlier work this paper cites.
V. Raykar, B. Krishnapuram, J. Bi, M. Dundar, and R. Rao, “Bayesian multiple instance learning: automatic feature selection and inductive transfer,” in ICML
2008
Earlier work this paper cites.
C. D. Johnson, M.-H. Chen, A. Y. Toledano, J. P. Heiken, A. Dachman, M. D. Kuo, C. O. Menias, B. Siewert, J. I. Cheema, R. G. Obregon, et al
2008
Earlier work this paper cites.
J. Yao, J. Li, and R. M. Summers, “Employing topographical height map in colonic polyp measurement and false positive reduction,” Pattern Recognition
2009
Earlier work this paper cites.
L. Lu, M. Wolf, J. Liang, M. Dundar, J. Bi, and M. Salganicoff, “A two-level approach towards semantic colon segmentation: Removing extra-colonic findings,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI
2009
Earlier work this paper cites.
M. Feuerstein, D. Deguchi, T. Kitasaka, S. Iwano, K. Imaizumi, Y. Hasegawa, Y. Suenaga, and K. Mori, “Automatic mediastinal lymph node detection in chest CT,” SPIE Med. Imag
2009
Earlier work this paper cites.
D. Wu, L. Lu, J. Bi, Y. Shinagawa, K. Boyer, A. Krishnan, and M. Salganicoff, “Stratified learning of local anatomical context for lung nodules in CT images,” in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on
2010
Earlier work this paper cites.
V. Ravesteijn, C. Wijk, F. Vos, R. Truyen, J. Peters, J. Stoker, and L. Vliet, “Computer aided detection of polyps in ct colonography using logistic regression,” Medical Imaging, IEEE Transactions on
2010
Earlier work this paper cites.
S. C. Turaga, J. F. Murray, V. Jain, F. Roth, M. Helmstaedter, K. Briggman, W. Denk, and H. S. Seung, “Convolutional networks can learn to generate affinity graphs for image segmentation,” Neural Computation
2010
Earlier work this paper cites.
G. Slabaugh, X. Yang, X. Ye, R. Boyes, and G. Beddoe, “A robust and fast system for ctc computer-aided detection of colorectal lesions,” Algorithms
2010
Cited alongside, same era.
L. Lu, M. Liu, X. Ye, S. Yu, and H. Huang, “Coarse-to-fine classification via parametric and nonparametric models for computer-aided diagnosis,” in Proc. ACM Conf. on CIKM
2011
Cited alongside, same era.
T. Wiese, J. Burns, J. Yao, and R. M. Summers, “Computer-aided detection of sclerotic bone metastases in the spine using watershed algorithm and support vector machines,” in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
2011
Cited alongside, same era.
L. Lu, J. Bi, M. Wolf, and M. Salganicoff, “Effective 3D object detection and regression using probabilistic segmentation features in CT images,” in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
2011
Cited alongside, same era.
J. N., “Computer science: The learning machines,” Nature
2014
Later among the works it cites.
H. R. Roth, L. Lu, A. Seff, K. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey, and R. M. Summers, “A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
2014
Later among the works it cites.
Q. Li, W. Cai, X. Wang, Y. Zhou, D. D. Feng, and M. Chen, “Medical image classification with convolutional neural network,” ICARCV
2014
Later among the works it cites.
K. M. Cherry, S. Wang, E. B. Turkbey, and R. M. Summers, “Abdominal lymphadenopathy detection using random forest,” SPIE Med. Imag
2014
Later among the works it cites.
J. Liu, J. Zhao, J. Hoffman, J. Yao, W. Zhang, E. B. Turkbey, S. Wang, C. Kim, and R. M. Summers, “Mediastinal lymph node detection on thoracic CT scans using spatial prior from multi-atlas label fusion,” SPIE Med. Imag
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Chang and C. Lin, “Libsvm: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology
2011
Cited alongside, same era.
T. Wiese, J. Yao, J. E. Burns, and R. M. Summers, “Detection of sclerotic bone metastases in the spine using watershed algorithm and graph cut,” in SPIE Med. Imag
2012
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural networks,” NIPS
2012
Cited alongside, same era.
D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Deep neural networks segment neuronal membranes in electron microscopy images,” in Advances in neural information processing systems
2012
Cited alongside, same era.
2012
Cited alongside, same era.
A. Barbu, M. Suehling, X. Xu, D. Liu, S. K. Zhou, and D. Comaniciu, “Automatic detection and segmentation of lymph nodes from CT data,” Medical Imaging, IEEE Transactions on
2012
Cited alongside, same era.
J. E. Burns, J. Yao, T. S. Wiese, H. E. Muñoz, E. C. Jones, and R. M. Summers, “Automated detection of sclerotic metastases in the thoracolumbar spine at CT,” Radiology
2013
Cited alongside, same era.
M. Hammon, P. Dankerl, A. Tsymbal, M. Wels, M. Kelm, M. May, M. Suehling, M. Uder, and A. Cavallaro, “Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography,” European radiology
2013
Cited alongside, same era.
2014
Later among the works it cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res
2014
Later among the works it cites.
2014
Later among the works it cites.
L. Lu, P. Devarakota, S. Vikal, D. Wu, Y. Zheng, and M. Wolf, “Computer aided diagnosis using multilevel image features on large-scale evaluation,” in Medical Computer Vision. Large Data in Medical Imaging
2014
Later among the works it cites.
K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos,” in Advances in Neural Information Processing System
2014
Later among the works it cites.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks.,” in Proc. IEEE Conf. on CVPR
2014
Later among the works it cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” in Advances in Neural Information Processing Systems
2014
Later among the works it cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
B. van Ginneken, A. Setio, C. Jacobs, and F. Ciompi, “Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans,” in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
2015
Closest in time.
H. Roth, J. Yao, L. Lu, J. Stieger, J. Burns, and R. Summers, “Detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications,” in Recent Advances in Computational Methods and Clinical Applications for Spine Imaging
2015
Closest in time.
N. Tajbakhsh, M. B. Gotway, and J. Liang, “Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks,” in MICCAI
2015
Closest in time.
W. Shen, M. Zhou, F. Yang, C. Yang, and J. Tian, “Multi-scale convolutional neural networks for lung nodule classification,” in Information Processing in Medical Imaging
2015
Closest in time.
S. Park, M. Lee, and N. Kwak, “Polyp detection in colonoscopy videos using deeply-learned hierarchical features,” in Seoul National University
2015
Closest in time.
N. Tajbakhsh, S. Gurudu, and J. Liang, “A comprehensive computer-aided polyp detection system for colonoscopy videos,” in Information Processing in Medical Imaging
2015
Closest in time.