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The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
1907
Earlier work this paper cites.
Lin G, Milan A, Shen C, Reid I (2017a) RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1925–1934
1934
Earlier work this paper cites.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958
1958
Earlier work this paper cites.
Mohajerani S, Asad R, Abhishek K, Sharma N, van Duynhoven A, Saeedi P (2019) Cloudmaskgan: A content-aware unpaired image-to-image translation algorithm for remote sensing imagery. In: 2019 IEEE International Conference on Image Processing, IEEE, pp 1965–1969
1969
Earlier work this paper cites.
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536, DOI 10.1038/323533a0
1986
Earlier work this paper cites.
Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 42(5):577–685
1989
Earlier work this paper cites.
Haralick RM, Shapiro LG (1992) Computer and Robot Vision. Addison-Wesley
1992
Earlier work this paper cites.
Jensen J, Svendsen N (1992) Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 39(2):262–267, DOI 10.1109/58.139123
1992
Earlier work this paper cites.
Bengio Y, Frasconi P (1994) Credit assignment through time: Alternatives to backpropagation. In: Advances in Neural Information Processing Systems, pp 75–82
1994
Earlier work this paper cites.
Jensen JA (1996) Field: A program for simulating ultrasound systems. In: 10th Nordic-Baltic Conference on Biomedical Imaging, Volume 34, Supplement 1, Part 1, pp 351–353
1996
Earlier work this paper cites.
Caruana R (1997) Multitask learning. Machine Learning 28(1):41–75
1997
Earlier work this paper cites.
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computation 9(8):1735–1780
1997
Earlier work this paper cites.
Reddick W, Glass J, Cook E, Elkin T, Deaton R (1997) Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks. IEEE Transactions on Medical Imaging 16(6):911–918, DOI 10.1109/42.650887
1997
Earlier work this paper cites.
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278–2324, DOI 10.1109/5.726791
1998
Earlier work this paper cites.
Kuntimad G, Ranganath H (1999) Perfect image segmentation using pulse coupled neural networks. IEEE Transactions on Neural Networks 10(3):591–598, DOI 10.1109/72.761716
1999
Earlier work this paper cites.
Stanley KO, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evolutionary computation 10(2):99–127
2002
Earlier work this paper cites.
Adams RA, Fournier JJ (2003) Sobolev spaces. Elsevier
2003
Earlier work this paper cites.
Simard PY, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2, IEEE Computer Society, USA, ICDAR ’03, p 958
2003
Earlier work this paper cites.
Benoit-Cattin H, Collewet G, Belaroussi B, Saint-Jalmes H, Odet C (2005) The SIMRI project: a versatile and interactive MRI simulator. Journal of Magnetic Resonance 173(1):97–115, DOI 10.1016/j.jmr.2004.09.027
2004
Earlier work this paper cites.
Reilhac A, Batan G, Michel C, Grova C, Tohka J, Collins D, Costes N, Evans A (2005) PET-SORTEO: validation and development of database of simulated PET volumes. IEEE Transactions on Nuclear Science 52(5):1321–1328, DOI 10.1109/tns.2005.858242
2005
Earlier work this paper cites.
Drobnjak I, Gavaghan D, Süli E, Pitt-Francis J, Jenkinson M (2006) Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts. Magnetic Resonance in Medicine 56(2):364–380, DOI 10.1002/mrm.20939
2006
Earlier work this paper cites.
Tabary J, Hugonnard P, Mathy F (2007) SINDBAD: a realistic multi-purpose and scalable X-ray simulation tool for NDT applications. In: DIR 2007: International Symposium on Digital Industrial Radiology and Computed Tomography
2007
Earlier work this paper cites.
Brostow GJ, Shotton J, Fauqueur J, Cipolla R (2008) Segmentation and recognition using structure from motion point clouds. In: Lecture Notes in Computer Science, Springer Berlin Heidelberg, pp 44–57, DOI 10.1007/978-3-540-88682-2_5
2008
Earlier work this paper cites.
Brostow GJ, Fauqueur J, Cipolla R (2009) Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters 30(2):88–97, DOI 10.1016/j.patrec.2008.04.005
2008
Earlier work this paper cites.
Russell BC, Torralba A, Murphy KP, Freeman WT (2008) LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision 77(1-3):157–173, DOI 10.1007/s11263-007-0090-8
2008
Earlier work this paper cites.
Drobnjak I, Pell GS, Jenkinson M (2010) Simulating the effects of time-varying magnetic fields with a realistic simulated scanner. Magnetic Resonance Imaging 28(7):1014–1021, DOI 10.1016/j.mri.2010.03.029
2010
Earlier work this paper cites.
Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. International Journal of Computer Vision 88(2):303–338, DOI 10.1007/s11263-009-0275-4
2010
Earlier work this paper cites.
Hamarneh G, Jassi P (2010) VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis. Computerized Medical Imaging and Graphics 34(8):605–616, DOI 10.1016/j.compmedimag.2010.06.002
2010
Earlier work this paper cites.
Xiao J, Hays J, Ehinger KA, Oliva A, Torralba A (2010) SUN database: Large-scale scene recognition from abbey to zoo. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp 3485–3492
2010
Earlier work this paper cites.
Cireşan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) High-performance neural networks for visual object classification. arXiv preprint arXiv:11020183
2011
Earlier work this paper cites.
Marion A, Forestier G, Benoit-Cattin H, Camarasu-Pop S, Clarysse P, da Silva RF, Gibaud B, Glatard T, Hugonnard P, Lartizien C, Liebgott H, Specovius S, Tabary J, Valette S, Friboulet D (2011) Multi-modality medical image simulation of biological models with the virtual imaging platform (VIP). In: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), IEEE, DOI 10.1109/cbms.2011.5999141
2011
Earlier work this paper cites.
Cireşan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 3642–3649
2012
Earlier work this paper cites.
Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2012) The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
2012
Earlier work this paper cites.
Glatard T, Lartizien C, Gibaud B, da Silva RF, Forestier G, Cervenansky F, Alessandrini M, Benoit-Cattin H, Bernard O, Camarasu-Pop S, Cerezo N, Clarysse P, Gaignard A, Hugonnard P, Liebgott H, Marache S, Marion A, Montagnat J, Tabary J, Friboulet D (2013) A virtual imaging platform for multi-modality medical image simulation. IEEE Transactions on Medical Imaging 32(1):110–118, DOI 10.1109/tmi.2012.2220154
2012
Earlier work this paper cites.
Harrison R, Lewellen T (2012) The SimSET program. In: Monte Carlo Calculations in Nuclear Medicine, Second Edition, Taylor & Francis, pp 87–110, DOI 10.1201/b13073-7
2012
Earlier work this paper cites.
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp 1097–1105
2012
Earlier work this paper cites.
Couprie C, Farabet C, Najman L, LeCun Y (2013) Indoor semantic segmentation using depth information. arXiv preprint arXiv:13013572
2013
Earlier work this paper cites.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp 2672–2680
2014
Earlier work this paper cites.
Mottaghi R, Chen X, Liu X, Cho NG, Lee SW, Fidler S, Urtasun R, Yuille A (2014) The role of context for object detection and semantic segmentation in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 891–898
2014
Earlier work this paper cites.
Nowozin S (2014) Optimal decisions from probabilistic models: the intersection-over-union case. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 548–555
2014
Earlier work this paper cites.
Sifre L (2014) Rigid-motion scattering for image classification. PhD thesis, CMAP, Ecole Polytechnique
2014
Earlier work this paper cites.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556
2014
Earlier work this paper cites.
Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using Places database. In: Advances in Neural Information Processing Systems, pp 487–495
2014
Earlier work this paper cites.
Badrinarayanan V, Handa A, Cipolla R (2015) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv:151100561
2015
Earlier work this paper cites.
Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The PASCAL visual object classes challenge: A retrospective. International Journal of Computer Vision 111(1):98–136
2015
Earlier work this paper cites.
Kim YD, Park E, Yoo S, Choi T, Yang L, Shin D (2015) Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:151106530
2015
Earlier work this paper cites.
Lee DH, Zhang S, Fischer A, Bengio Y (2015) Difference target propagation. In: Machine Learning and Knowledge Discovery in Databases, Springer International Publishing, pp 498–515, DOI 10.1007/978-3-319-23528-8_31
2015
Earlier work this paper cites.
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3431–3440
2015
Earlier work this paper cites.
Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1520–1528
2015
Earlier work this paper cites.
Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp 91–99
2015
Earlier work this paper cites.
Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, pp 234–241
2015
Earlier work this paper cites.
Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks. arXiv preprint arXiv:150500387
2015
Earlier work this paper cites.
Xie S, Tu Z (2015) Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1395–1403
2015
Earlier work this paper cites.
Zhen X, Li S (2015) Towards direct medical image analysis without segmentation. arXiv preprint arXiv:151006375
2015
Earlier work this paper cites.
BenTaieb A, Hamarneh G (2016) Topology aware fully convolutional networks for histology gland segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, pp 460–468
2016
Earlier work this paper cites.
Chen LC, Yang Y, Wang J, Xu W, Yuille AL (2016) Attention to scale: Scale-aware semantic image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3640–3649
2016
Earlier work this paper cites.
Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3213–3223
2016
Earlier work this paper cites.
Han S, Liu X, Mao H, Pu J, Pedram A, Horowitz MA, Dally WJ (2016) EIE: efficient inference engine on compressed deep neural network. In: 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture, IEEE, pp 243–254
2016
Earlier work this paper cites.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778
2016
Earlier work this paper cites.
Honari S, Yosinski J, Vincent P, Pal C (2016) Recombinator networks: Learning coarse-to-fine feature aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5743–5752
2016
Earlier work this paper cites.
Kim HE, Hwang S (2016) Deconvolutional feature stacking for weakly-supervised semantic segmentation. arXiv preprint arXiv:160204984
2016
Earlier work this paper cites.
Luc P, Couprie C, Chintala S, Verbeek J (2016) Semantic segmentation using adversarial networks. arXiv preprint arXiv:161108408
2016
Earlier work this paper cites.
Milletari F, Navab N, Ahmadi SA (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision, IEEE, pp 565–571
2016
Earlier work this paper cites.
Nosrati MS, Hamarneh G (2016) Incorporating prior knowledge in medical image segmentation: a survey. arXiv preprint arXiv:160701092
2016
Earlier work this paper cites.
Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,
2016
Earlier work this paper cites.
Saxena S, Verbeek J (2016) Convolutional neural fabrics. In: Advances in Neural Information Processing Systems, pp 4053–4061
2016
Earlier work this paper cites.
Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. arXiv preprint arXiv:161101578
2016
Earlier work this paper cites.
Abdulla W (2017) Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. https://github.com/matterport/Mask_RCNN
2017
Earlier work this paper cites.
Amirul Islam M, Rochan M, Bruce ND, Wang Y (2017) Gated feedback refinement network for dense image labeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3751–3759
2017
Cited alongside, same era.
Chaichulee S, Villarroel M, Jorge J, Arteta C, Green G, McCormick K, Zisserman A, Tarassenko L (2017) Multi-task convolutional neural network for patient detection and skin segmentation in continuous non-contact vital sign monitoring. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition, IEEE, pp 266–272
2017
Cited alongside, same era.
Chartsias A, Joyce T, Dharmakumar R, Tsaftaris SA (2017) Adversarial image synthesis for unpaired multi-modal cardiac data. In: International Workshop on Simulation and Synthesis in Medical Imaging, Springer, pp 3–13
2017
Cited alongside, same era.
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1251–1258
Xue Y, Xu T, Zhang H, Long LR, Huang X (2018) SegAN: Adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 16(3-4):383–392
2018
Later among the works it cites.
Yang Q, Li N, Zhao Z, Fan X, Chang EI, Xu Y, et al. (2018) MRI cross-modality neuroimage-to-neuroimage translation. arXiv preprint arXiv:180106940
2018
Later among the works it cites.
Yu B, Zhou L, Wang L, Fripp J, Bourgeat P (2018a) 3D cGAN based cross-modality MR image synthesis for brain tumor segmentation. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, IEEE, pp 626–630
2018
Later among the works it cites.
Zamir AR, Sax A, Shen W, Guibas LJ, Malik J, Savarese S (2018) Taskonomy: Disentangling task transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3712–3722
2018
Later among the works it cites.
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2017
Cited alongside, same era.
Costa P, Galdran A, Meyer MI, Abràmoff MD, Niemeijer M, Mendonça AM, Campilho A (2017) Towards adversarial retinal image synthesis. arXiv preprint arXiv:170108974
2017
Cited alongside, same era.
Czarnecki WM, Osindero S, Jaderberg M, Swirszcz G, Pascanu R (2017) Sobolev training for neural networks. In: Advances in Neural Information Processing Systems, pp 4278–4287
2017
Cited alongside, same era.
Galdran A, Alvarez-Gila A, Meyer MI, Saratxaga CL, Araújo T, Garrote E, Aresta G, Costa P, Mendonça AM, Campilho A (2017) Data-driven color augmentation techniques for deep skin image analysis. arXiv preprint arXiv:170303702
2017
Cited alongside, same era.
Goceri E, Goceri N (2017) Deep learning in medical image analysis: Recent advances and future trends. In: Proceedings of the IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP) 2017, pp 305–310
2017
Cited alongside, same era.
Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein gans. In: Advances in Neural Information Processing Systems, pp 5767–5777
2017
Cited alongside, same era.
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2961–2969
2017
Cited alongside, same era.
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:170404861
2017
Cited alongside, same era.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4700–4708
2017
Cited alongside, same era.
Zhao T, Yang Y, Niu H, Wang D, Chen Y (2018) Comparing U-Net convolutional network with Mask R-CNN in the performances of pomegranate tree canopy segmentation. In: Asia-Pacific Remote Sensing
2018
Later among the works it cites.
Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems 30(11):3212–3232, DOI 10.1109/tnnls.2018.2876865
2018
Later among the works it cites.
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) UNet++: A nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, pp 3–11
2018
Later among the works it cites.
Zhu X, Zhou H, Yang C, Shi J, Lin D (2018) Penalizing top performers: Conservative loss for semantic segmentation adaptation. In: Proceedings of the European Conference on Computer Vision, pp 568–583
2018
Later among the works it cites.
Abhishek K, Hamarneh G (2019) Mask2Lesion: Mask-constrained adversarial skin lesion image synthesis. Medical Image Computing and Computer-Assisted Intervention Workshop on Simulation and Synthesis in Medical Imaging pp 71–80
2019
Closest in time.
Afshari S, BenTaieb A, Mirikharaji Z, Hamarneh G (2019) Weakly supervised fully convolutional network for PET lesion segmentation. In: Medical Imaging 2019: Image Processing, International Society for Optics and Photonics, vol 10949, p 109491K
2019
Closest in time.
Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK (2019) Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging 6(1):14006
2019
Closest in time.
Amit Y (2019) Deep learning with asymmetric connections and hebbian updates. Frontiers in Computational Neuroscience 13, DOI 10.3389/fncom.2019.00018
2019
Closest in time.
Bellec G, Scherr F, Hajek E, Salaj D, Legenstein R, Maass W (2019) Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets. arXiv preprint arXiv:190109049
2019
Closest in time.
Bischke B, Helber P, Folz J, Borth D, Dengel A (2019) Multi-task learning for segmentation of building footprints with deep neural networks. In: 2019 IEEE International Conference on Image Processing, IEEE, pp 1480–1484
2019
Closest in time.
Bonta LR, Kiran NU (2019) Efficient segmentation of medical images using dilated residual networks. In: Computer Aided Intervention and Diagnostics in Clinical and Medical Images, Springer, pp 39–47
2019
Closest in time.
Borji A, Cheng MM, Hou Q, Jiang H, Li J (2019) Salient object detection: A survey. Computational Visual Media 5(2):117–150, DOI 10.1007/s41095-019-0149-9
2019
Closest in time.
Brügger R, Baumgartner CF, Konukoglu E (2019) A partially reversible U-Net for memory-efficient volumetric image segmentation. arXiv preprint arXiv:190606148
2019
Closest in time.
Caliva F, Iriondo C, Martinez AM, Majumdar S, Pedoia V (2019) Distance map loss penalty term for semantic segmentation. International Conference on Medical Imaging with Deep Learning
2019
Closest in time.
Chen X, Williams BM, Vallabhaneni SR, Czanner G, Williams R, Zheng Y (2019) Learning active contour models for medical image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 11632–11640
2019
Closest in time.
Cherian A, Sullivan A (2019) Sem-GAN: Semantically-consistent image-to-image translation. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, DOI 10.1109/wacv.2019.00196
2019
Closest in time.
Choi J, Kim T, Kim C (2019) Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 6830–6840
2019
Closest in time.
Gamage H, Wijesinghe W, Perera I (2019) Instance-based segmentation for boundary detection of neuropathic ulcers through Mask-RCNN. In: International Conference on Artificial Neural Networks, Springer, pp 511–522
2019
Closest in time.
Goceri E (2019a) Challenges and recent solutions for image segmentation in the era of deep learning. In: 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), IEEE, DOI 10.1109/ipta.2019.8936087
2019
Closest in time.
Goceri E (2020) CapsNet topology to classify tumours from brain images and comparative evaluation. IET Image Processing 14(5):882–889, DOI 10.1049/iet-ipr.2019.0312
2019
Closest in time.
Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, Talbott J, Zhuoquiong R, Liu Y, Granberg T, et al. (2019) Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 184:901–915
2019
Closest in time.
Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) CE-Net: Context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging
2019
Closest in time.
Han C, Murao K, Satoh S, Nakayama H (2019) Learning more with less: GAN-based medical image augmentation. Medical Imaging Technology 37(3):137–142
2019
Closest in time.
He T, Guo J, Wang J, Xu X, Yi Z (2019) Multi-task learning for the segmentation of thoracic organs at risk in CT images. In: SegTHOR@ISBI
2019
Closest in time.
Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: Achievements and challenges. Journal of Digital Imaging pp 1–15
2019
Closest in time.
Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G, Norajitra T, Wirkert S, et al. (2019) nnU-Net: Self-adapting framework for U-Net-based medical image segmentation. In: Bildverarbeitung für die Medizin 2019, Springer, pp 22–22
2019
Closest in time.
Karimi D, Salcudean SE (2019) Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks. arXiv preprint arXiv:190410030
2019
Closest in time.
Karimi D, Dou H, Warfield SK, Gholipour A (2019) Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. arXiv preprint arXiv:191202911
2019
Closest in time.
Kervadec H, Dolz J, Tang M, Granger E, Boykov Y, Ayed IB (2019b) Constrained-CNN losses for weakly supervised segmentation. Medical Image Analysis 54:88–99, DOI https://doi.org/10.1016/j.media.2019.02.009
2019
Closest in time.
Khosravan N, Mortazi A, Wallace M, Bagci U (2019) PAN: Projective adversarial network for medical image segmentation. arXiv preprint arXiv:190604378
2019
Closest in time.
Kim B, Ye JC (2019) Multiphase level-set loss for semi-supervised and unsupervised segmentation with deep learning. arXiv preprint arXiv:190402872
2019
Closest in time.
Lateef F, Ruichek Y (2019) Survey on semantic segmentation using deep learning techniques. Neurocomputing 338:321–348
2019
Closest in time.
Le TLT, Thome N, Bernard S, Bismuth V, Patoureaux F (2019) Multitask classification and segmentation for cancer diagnosis in mammography. arXiv preprint arXiv:190905397
2019
Closest in time.
Lee J, Kim E, Lee S, Lee J, Yoon S (2019) Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5267–5276
2019
Closest in time.
Ma WDK, Lewis J, Kleijn WB (2019) The hsic bottleneck: Deep learning without back-propagation. arXiv preprint arXiv:190801580
2019
Closest in time.
Mirikharaji Z, Yan Y, Hamarneh G (2019) Learning to segment skin lesions from noisy annotations. International Workshop on Medical Image Learning with Less Labels and Imperfect Data
2019
Closest in time.
Mukherjee S, Cheng I, Miller S, Guo T, Chau V, Basu A (2019) A fast segmentation-free fully automated approach to white matter injury detection in preterm infants. Medical & Biological Engineering & Computing 57(1):71–87
2019
Closest in time.
Ni ZL, Bian GB, Xie XL, Hou ZG, Zhou XH, Zhou YJ (2019) RASNet: Segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network. arXiv preprint arXiv:190508663
2019
Closest in time.
Nøkland A, Eidnes LH (2019) Training neural networks with local error signals. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International Conference on Machine Learning, PMLR, Long Beach, California, USA, Proceedings of Machine Learning Research, vol 97, pp 4839–4850, URL http://proceedings.mlr.press/v97/nokland19a.html
2019
Closest in time.
Paschali M, Gasperini S, Roy AG, Fang MYS, Navab N (2019) 3DQ: Compact quantized neural networks for volumetric whole brain segmentation. arXiv preprint arXiv:190403110
2019
Closest in time.
Peng J, Kervadec H, Dolz J, Ayed IB, Pedersoli M, Desrosiers C (2019) Discretely-constrained deep network for weakly supervised segmentation. arXiv preprint arXiv:190805770
2019
Closest in time.
Perone CS, Cohen-Adad J (2019) Promises and limitations of deep learning for medical image segmentation. Journal of Medical Artificial Intelligence 2
2019
Closest in time.
Perone CS, Ballester P, Barros RC, Cohen-Adad J (2019) Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. Neuroimage 194:1–11
2019
Closest in time.
Proenca H, Neves JC (2019) Segmentation-less and non-holistic deep-learning frameworks for iris recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 0–0
2019
Closest in time.
Raghu M, Zhang C, Kleinberg J, Bengio S (2019) Transfusion: Understanding transfer learning with applications to medical imaging. arXiv preprint arXiv:190207208
2019
Closest in time.
Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D (2019) Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Analysis 53:197–207
2019
Closest in time.
Shaw A, Hunter D, Landola F, Sidhu S (2019) SqueezeNAS: Fast neural architecture search for faster semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision Workshops
2019
Closest in time.
Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. Journal of Big Data 6(1), DOI 10.1186/s40537-019-0197-0
2019
Closest in time.
Simpson AL, Antonelli M, Bakas S, Bilello M, Farahani K, van Ginneken B, Kopp-Schneider A, Landman BA, Litjens G, Menze B, et al. (2019) A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:190209063
2019
Closest in time.
Sinha A, Dolz J (2019) Multi-scale guided attention for medical image segmentation. arXiv preprint arXiv:190602849
2019
Closest in time.
Tsai HF, Gajda J, Sloan TF, Rares A, Shen AQ (2019) Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning. SoftwareX 9:230–237
2019
Closest in time.
Vorontsov E, Molchanov P, Byeon W, De Mello S, Jampani V, Liu MY, Kadoury S, Kautz J (2019) Towards semi-supervised segmentation via image-to-image translation. arXiv preprint arXiv:190401636
2019
Closest in time.
Vuola AO, Akram SU, Kannala J (2019) Mask R-CNN and U-net ensembled for nuclei segmentation. arXiv preprint arXiv:190110170
2019
Closest in time.
Weng Y, Zhou T, Li Y, Qiu X (2019b) NAS-unet: Neural architecture search for medical image segmentation. IEEE Access 7:44247–44257, DOI 10.1109/access.2019.2908991
2019
Closest in time.
Wessel J, Heinrich MP, von Berg J, Franz A, Saalbach A (2019) Sequential rib labeling and segmentation in chest X-ray using Mask R-CNN. In: International Conference on Medical Imaging with Deep Learning – Extended Abstract Track, London, United Kingdom, URL https://openreview.net/forum?id=SJxuHzLjFV
2019
Closest in time.
Wu Z, Shen C, Van Den Hengel A (2019) Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recognition 90:119–133
2019
Closest in time.
Zhang P, Zhong Y, Deng Y, Tang X, Li X (2019) A survey on deep learning of small sample in biomedical image analysis. arXiv preprint arXiv:190800473
2019
Closest in time.
Zhu Z, Liu C, Yang D, Yuille A, Xu D (2019) V-NAS: Neural architecture search for volumetric medical image segmentation. In: 2019 International Conference on 3D Vision (3DV), IEEE, DOI 10.1109/3dv.2019.00035
2019
Closest in time.
Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: A survey. arXiv preprint arXiv:190505055
2019
Closest in time.
Abhishek K, Hamarneh G, Drew MS (2020) Illumination-based transformations improve skin lesion segmentation in dermoscopic images. arXiv preprint arXiv:200310111
2020
Closest in time.
Challenge G (2020) Grand challenges in biomedical image analysis. URL https://grand-challenge.org/challenges/
2020
Closest in time.
Jin W, Fatehi M, Abhishek K, Mallya M, Toyota B, Hamarneh G (2020) Artificial intelligence in glioma imaging: challenges and advances. Journal of Neural Engineering 17(2):021002, DOI 10.1088/1741-2552/ab8131
2020
Closest in time.
Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X (2020) Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis p 101693
2020
Closest in time.
Xie X, Niu J, Liu X, Chen Z, Tang S (2020) A survey on domain knowledge powered deep learning for medical image analysis. arXiv preprint arXiv:200412150
2020
Closest in time.
Wen W, Wu C, Wang Y, Chen Y, Li H (2016) Learning structured sparsity in deep neural networks. In: Advances in Neural Information Processing Systems, pp 2074–2082
2082
Closest in time.