Fetching the paper…
Reading the bibliography…
Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differences in contribution between different-level features and 2) designing an effective mechanism for fusing these features.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in AISTATS , 2011
2011
Earlier work this paper cites.
J. Bernal, J. Sánchez, and F. Vilarino, “Towards automatic polyp detection with a polyp appearance model,” PR , vol. 45, no. 9, pp. 3166–3182, 2012
2012
Earlier work this paper cites.
M. Fiori, P. Musé, and G. Sapiro, “A complete system for candidate polyps detection in virtual colonoscopy,” IJPRAI , vol. 28, no. 07, p. 1460014, 2014
2014
Earlier work this paper cites.
A. V. Mamonov, I. N. Figueiredo, P. N. Figueiredo, and Y.-H. R. Tsai, “Automated polyp detection in colon capsule endoscopy,” IEEE TMI , vol. 33, no. 7, pp. 1488–1502, 2014
2014
Earlier work this paper cites.
J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, “Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,” IJCARS , vol. 9, no. 2, pp. 283–293, 2014
2014
Earlier work this paper cites.
R. Margolin, L. Zelnik-Manor, and A. Tal, “How to evaluate foreground maps?” in CVPR , 2014
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI , 2015
2015
Earlier work this paper cites.
J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, D. Gil, C. Rodríguez, and F. Vilariño, “Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,” CMIG , vol. 43, pp. 99–111, 2015
2015
Earlier work this paper cites.
N. Tajbakhsh, S. R. Gurudu, and J. Liang, “Automated polyp detection in colonoscopy videos using shape and context information,” IEEE TMI , vol. 35, no. 2, pp. 630–644, 2015
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in ICLR , 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in ICML , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang, “Convolutional neural networks for medical image analysis: Full training or fine tuning?” IEEE TMI , vol. 35, no. 5, pp. 1299–1312, 2016
2016
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3DV , 2016
2016
Earlier work this paper cites.
O. H. Maghsoudi, “Superpixel based segmentation and classification of polyps in wireless capsule endoscopy,” in IEEE SPMB , 2017
2017
Earlier work this paper cites.
D. Vázquez, J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, A. M. López, A. Romero, M. Drozdzal, and A. Courville, “A benchmark for endoluminal scene segmentation of colonoscopy images,” JHE , vol. 2017, 2017
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV , 2017
2017
Earlier work this paper cites.
Z. Zhou, J. Shin, L. Zhang, S. Gurudu, M. Gotway, and J. Liang, “Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally,” in CVPR , 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS , 2017
2017
Earlier work this paper cites.
W. Wang, X. Li, T. Lu, and J. Yang, “Mixed link networks,” in IJCAI , 2018
2018
Earlier work this paper cites.
M. Akbari, M. Mohrekesh, E. Nasr-Esfahani, S. R. Soroushmehr, N. Karimi, S. Samavi, and K. Najarian, “Polyp segmentation in colonoscopy images using fully convolutional network,” in IEEE EMBC , 2018
2018
Earlier work this paper cites.
P. Brandao, O. Zisimopoulos, E. Mazomenos, G. Ciuti, J. Bernal, M. Visentini-Scarzanella, A. Menciassi, P. Dario, A. Koulaouzidis, A. Arezzo et al. , “Towards a computed-aided diagnosis system in colonoscopy: automatic polyp segmentation using convolution neural networks,” JMRR , vol. 3, no. 02, p. 1840002, 2018
2018
Earlier work this paper cites.
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,” in DLMIA , 2018
2018
Earlier work this paper cites.
S. Woo, J. Park, J.-Y. Lee, and I. So Kweon, “Cbam: Convolutional block attention module,” in ECCV , 2018
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in CVPR , 2018
2018
Earlier work this paper cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in CVPR , 2018
2018
Earlier work this paper cites.
D.-P. Fan, C. Gong, Y. Cao, B. Ren, M.-M. Cheng, and A. Borji, “Enhanced-alignment measure for binary foreground map evaluation,” in IJCAI , 2018
2018
Earlier work this paper cites.
X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” in CVPR , 2019
2019
Earlier work this paper cites.
D. Jha, P. H. Smedsrud, M. A. Riegler, D. Johansen, T. de Lange, P. Halvorsen, and H. D. Johansen, “Resunet++: An advanced architecture for medical image segmentation,” in IEEE ISM , 2019
2019
Earlier work this paper cites.
X. Sun, P. Zhang, D. Wang, Y. Cao, and B. Liu, “Colorectal polyp segmentation by u-net with dilation convolution,” in IEEE ICMLA , 2019
2019
Earlier work this paper cites.
B. Murugesan, K. Sarveswaran, S. M. Shankaranarayana, K. Ram, J. Joseph, and M. Sivaprakasam, “Psi-net: Shape and boundary aware joint multi-task deep network for medical image segmentation,” in IEEE EMBC , 2019
2019
Earlier work this paper cites.
H. A. Qadir, Y. Shin, J. Solhusvik, J. Bergsland, L. Aabakken, and I. Balasingham, “Polyp detection and segmentation using mask r-cnn: Does a deeper feature extractor cnn always perform better?” in ISMICT , 2019
2019
Earlier work this paper cites.
P. Chao, C.-Y. Kao, Y.-S. Ruan, C.-H. Huang, and Y.-L. Lin, “Hardnet: A low memory traffic network,” in CVPR , 2019
2019
Cited alongside, same era.
Z. Wu, L. Su, and Q. Huang, “Cascaded partial decoder for fast and accurate salient object detection,” in CVPR , 2019
2019
Cited alongside, same era.
Y. Lu, Y. Chen, D. Zhao, and J. Chen, “Graph-fcn for image semantic segmentation,” in ISNN , 2019
2019
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in ICLR , 2019
2019
Cited alongside, same era.
Y. Fang, C. Chen, Y. Yuan, and K.-y. Tong, “Selective feature aggregation network with area-boundary constraints for polyp segmentation,” in MICCAI , 2019
2019
Cited alongside, same era.
D. V. Sang, T. Q. Chung, P. N. Lan, D. V. Hang, D. Van Long, and N. T. Thuy, “Ag-curesnest: A novel method for colon polyp segmentation,” in IEEE RIVF , 2021
2021
Closest in time.
C. Yang, X. Guo, M. Zhu, B. Ibragimov, and Y. Yuan, “Mutual-prototype adaptation for cross-domain polyp segmentation,” IEEE JBHI , 2021
2021
Closest in time.
D. Jha, P. H. Smedsrud, D. Johansen, T. de Lange, H. D. Johansen, P. Halvorsen, and M. A. Riegler, “A comprehensive study on colorectal polyp segmentation with resunet++, conditional random field and test-time augmentation,” IEEE JBHI , vol. 25, no. 6, pp. 2029–2040, 2021
2021
Closest in time.
D. Jha, N. K. Tomar, S. Ali, M. A. Riegler, H. D. Johansen, D. Johansen, T. de Lange, and P. Halvorsen, “Nanonet: Real-time polyp segmentation in video capsule endoscopy and colonoscopy,” in IEEE CBMS , 2021
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D.-P. Fan, G.-P. Ji, T. Zhou, G. Chen, H. Fu, J. Shen, and L. Shao, “Pranet: Parallel reverse attention network for polyp segmentation,” in MICCAI , 2020
2020
Cited alongside, same era.
X. Guo, C. Yang, Y. Liu, and Y. Yuan, “Learn to threshold: Thresholdnet with confidence-guided manifold mixup for polyp segmentation,” IEEE TMI , vol. 40, no. 4, pp. 1134–1146, 2020
2020
Cited alongside, same era.
D.-P. Fan, G.-P. Ji, G. Sun, M.-M. Cheng, J. Shen, and L. Shao, “Camouflaged object detection,” in CVPR , 2020
2020
Cited alongside, same era.
D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. de Lange, D. Johansen, and H. D. Johansen, “Kvasir-seg: A segmented polyp dataset,” in MMM , 2020
2020
Cited alongside, same era.
T. Rahim, M. A. Usman, and S. Y. Shin, “A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging,” CMIG , p. 101767, 2020
2020
Cited alongside, same era.
S. Alam, N. K. Tomar, A. Thakur, D. Jha, and A. Rauniyar, “Automatic polyp segmentation using u-net-resnet50,” in MediaEvalW , 2020
2020
Cited alongside, same era.
D. Banik, K. Roy, D. Bhattacharjee, M. Nasipuri, and O. Krejcar, “Polyp-net: A multimodel fusion network for polyp segmentation,” IEEE TIM , vol. 70, pp. 1–12, 2020
2020
Cited alongside, same era.
S. Li, X. Sui, X. Luo, X. Xu, L. Yong, and R. S. M. Goh, “Medical image segmentation using squeeze-and-expansion transformers,” in IJCAI , 2021
2021
Closest in time.
T. Kim, H. Lee, and D. Kim, “Uacanet: Uncertainty augmented context attention for polyp semgnetaion,” in ACM MM , 2021
2021
Closest in time.
V. Thambawita, S. A. Hicks, P. Halvorsen, and M. A. Riegler, “Divergentnets: Medical image segmentation by network ensemble,” in ISBI & EndoCV , 2021
2021
Closest in time.
G. Xiaoqing, Y. Chen, and Y. Yixuan, “Dynamic-weighting hierarchical segmentation network for medical images,” MIA , p. 102196, 2021
2021
Closest in time.
G.-P. Ji, Y.-C. Chou, D.-P. Fan, G. Chen, D. Jha, H. Fu, and L. Shao, “Pns-net: Progressively normalized self-attention network for video polyp segmentation,” in MICCAI , 2021
2021
Closest in time.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2021
2021
Closest in time.
Z. Pan, B. Zhuang, J. Liu, H. He, and J. Cai, “Scalable visual transformers with hierarchical pooling,” in ICCV , 2021
2021
Closest in time.
B. Heo, S. Yun, D. Han, S. Chun, J. Choe, and S. J. Oh, “Rethinking spatial dimensions of vision transformers,” in ICCV , 2021
2021
Closest in time.
L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z. Jiang, F. E. Tay, J. Feng, and S. Yan, “Tokens-to-token vit: Training vision transformers from scratch on imagenet,” in ICCV , 2021
2021
Closest in time.
K. Han, A. Xiao, E. Wu, J. Guo, C. Xu, and Y. Wang, “Transformer in transformer,” Advances in Neural Information Processing Systems , vol. 34, pp. 15 908–15 919, 2021
2021
Closest in time.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” in ICCV , 2021
2021
Closest in time.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in ICCV , 2021
2021
Closest in time.
H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, and L. Zhang, “Cvt: Introducing convolutions to vision transformers,” in ICCV , 2021
2021
Closest in time.
W. Xu, Y. Xu, T. Chang, and Z. Tu, “Co-scale conv-attentional image transformers,” in ICCV , 2021
2021
Closest in time.
X. Chu, Z. Tian, Y. Wang, B. Zhang, H. Ren, X. Wei, H. Xia, and C. Shen, “Twins: Revisiting the design of spatial attention in vision transformers,” Advances in Neural Information Processing Systems , vol. 34, pp. 9355–9366, 2021
2021
Closest in time.
B. Graham, A. El-Nouby, H. Touvron, P. Stock, A. Joulin, H. Jégou, and M. Douze, “Levit: a vision transformer in convnet’s clothing for faster inference,” in ICCV , 2021
2021
Closest in time.
S. Bhojanapalli, A. Chakrabarti, D. Glasner, D. Li, T. Unterthiner, and A. Veit, “Understanding robustness of transformers for image classification,” in ICCV , 2021
2021
Closest in time.
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems , vol. 34, pp. 12 077–12 090, 2021
2021
Closest in time.
M.-M. Chen and D.-P. Fan, “Structure-measure: A new way to evaluate foreground maps,” IJCV , vol. 129, pp. 2622–2638, 2021
2021
Closest in time.
D.-P. Fan, G.-P. Ji, X. Qin, and M.-M. Cheng, “Cognitive vision inspired object segmentation metric and loss function,” SSI , 2021
2021
Closest in time.
L. Cai, M. Wu, L. Chen, W. Bai, M. Yang, S. Lyu, and Q. Zhao, “Using guided self-attention with local information for polyp segmentation,” in MICCAI . Springer, 2022
2022
Closest in time.
N. K. Tomar, D. Jha, U. Bagci, and S. Ali, “Tganet: Text-guided attention for improved polyp segmentation,” in MICCAI . Springer, 2022
2022
Closest in time.
R. Zhang, P. Lai, X. Wan, D.-J. Fan, F. Gao, X.-J. Wu, and G. Li, “Lesion-aware dynamic kernel for polyp segmentation,” in MICCAI . Springer, 2022
2022
Closest in time.
J.-H. Shi, Q. Zhang, Y.-H. Tang, and Z.-Q. Zhang, “Polyp-mixer: An efficient context-aware mlp-based paradigm for polyp segmentation,” IEEE TCSVT , 2022
2022
Closest in time.
X. Zhao, Z. Wu, S. Tan, D.-J. Fan, Z. Li, X. Wan, and G. Li, “Semi-supervised spatial temporal attention network for video polyp segmentation,” in MICCAI . Springer, 2022
2022
Closest in time.
Z. Yin, K. Liang, Z. Ma, and J. Guo, “Duplex contextual relation network for polyp segmentation,” in IEEE ISBI , 2022
2022
Closest in time.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pvt v2: Improved baselines with pyramid vision transformer,” CVMJ , vol. 8, no. 3, pp. 415–424, 2022
2022
Closest in time.
G.-P. Ji, G. Xiao, Y.-C. Chou, D.-P. Fan, K. Zhao, G. Chen, and L. Van Gool, “Video polyp segmentation: A deep learning perspective,” MIR , vol. 19, no. 06, pp. 531–549, 2022
2022
Closest in time.