Fetching the paper…
Reading the bibliography…
Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture.
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
Huang, H.; Lin, L.; Tong, R.; Hu, H.; Zhang, Q.; Iwamoto, Y.; Han, X.; Chen, Y.-W.; and Wu, J. 2020 · 2004
Earlier work this paper cites.
An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; and Houlsby, N. 2020 · 2010
Earlier work this paper cites.
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Zheng, S.; Lu, J.; Zhao, H.; Zhu, X.; Luo, Z.; Wang, Y.; Fu, Y.; Feng, J.; Xiang, T.; Torr, P. H. S.; and Zhang, L. 2020 · 2012
Earlier work this paper cites.
Segmentation Outside the Cranial Vault Challenge
Bennett, L.; Zhoubing, X.; Juan, I., Eugenio; Martin, S.; Thomas, L., Robin; and Arno, K. 2015 · 2015
Earlier work this paper cites.
Fully Convolutional Networks for Semantic Segmentation
Long, J.; Shelhamer, E.; and Darrell, T. 2015 · 2015
Earlier work this paper cites.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
Earlier work this paper cites.
The Importance of Skip Connections in Biomedical Image Segmentation
Drozdzal, M.; Vorontsov, E.; Chartrand, G.; Kadoury, S.; and Pal, C. 2016 · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F.; Navab, N.; and Ahmadi, S.-A. 2016 · 2016
Earlier work this paper cites.
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Sirinukunwattana, K.; Pluim, J. P. W.; Chen, H.; Qi, X.; Heng, P.-A.; Guo, Y. B.; Wang, L. Y.; Matuszewski, B. J.; Bruni, E.; Sanchez, U.; Böhm, A.; Ronneberger, O.; Cheikh, B. B.; Racoceanu, D.; Kainz, P.; Pfeiffer, M.; Urschler, M.; Snead, D. R. J.; and Rajpoot, N. M. 2016 · 2016
Earlier work this paper cites.
Densely Connected Convolutional Networks
Huang, G.; Liu, Z.; Van Der Maaten, L.; and Weinberger, K. Q. 2017 · 2017
Earlier work this paper cites.
A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
Kumar, N.; Verma, R.; Sharma, S.; Bhargava, S.; Vahadane, A.; and Sethi, A. 2017 · 2017
Cited alongside, same era.
Instance Normalization: The Missing Ingredient for Fast Stylization
Ulyanov, D.; Vedaldi, A.; and Lempitsky, V. 2017 · 2017
Cited alongside, same era.
Alom, M. Z.; Hasan, M.; Yakopcic, C.; Taha, T. M.; and Asari, V. K. 2018 · 2018
Cited alongside, same era.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
Li, X.; Chen, H.; Qi, X.; Dou, Q.; Fu, C.-W.; and Heng, P.-A. 2018 · 2018
Cited alongside, same era.
Segmenting Medical MRI via Recurrent Decoding Cell
Wen, Y.; Xie, K.; and He, L. 2020 · 2020
Later among the works it cites.
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
Cao, H.; Wang, Y.; Chen, J.; Jiang, D.; Zhang, X.; Tian, Q.; and Wang, M. 2021 · 2021
Closest in time.
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Chen, J.; Lu, Y.; Yu, Q.; Luo, X.; Adeli, E.; Wang, Y.; Lu, L.; Yuille, A. L.; and Zhou, Y. 2021 · 2021
Closest in time.
UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
Gao, Y.; Zhou, M.; and Metaxas, D. 2021 · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Oktay, O.; Schlemper, J.; Folgoc, L. L.; Lee, M.; Heinrich, M.; Misawa, K.; Mori, K.; McDonagh, S.; Hammerla, N. Y.; Kainz, B.; Glocker, B.; and Rueckert, D. 2018 · 2018
Cited alongside, same era.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zhou, Z.; Siddiquee, M. R.; Tajbakhsh, N.; and Liang, J. 2018 · 2018
Cited alongside, same era.
Domain adaptive relational reasoning for 3d multi-organ segmentation
Fu, S.; Lu, Y.; Wang, Y.; Zhou, Y.; Shen, W.; Fishman, E.; and Yuille, A. 2020 · 2020
Cited alongside, same era.
A Multi-Organ Nucleus Segmentation Challenge
Kumar, N.; Verma, R.; Anand, D.; Zhou, Y.; Onder, O. F.; Tsougenis, E.; Chen, H.; Heng, P.-A.; Li, J.; and Hu, Z. 2020 · 2020
Cited alongside, same era.
Pyramid Attention Aggregation Network for Semantic Segmentation of Surgical Instruments
Ni, Z.-L.; Bian, G.-B.; Wang, G.-A.; Zhou, X.-H.; Hou, Z.-G.; Chen, H.-B.; and Xie, X.-L. 2020 · 2020
Cited alongside, same era.
MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
Rahman, M. S. 2020 · 2020
Cited alongside, same era.
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Wang, Q.; Wu, B.; Zhu, P.; Li, P.; Zuo, W.; and Hu, Q. 2020 · 2020
Cited alongside, same era.
Gao, Z.; Hong, B.; Zhang, X.; Li, Y.; Jia, C.; Wu, J.; Wang, C.; Meng, D.; and Li, C. 2021 · 2021
Closest in time.
UNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A.; Yang, D.; Roth, H.; and Xu, D. 2021 · 2021
Closest in time.
Multi-Compound Transformer for Accurate Biomedical Image Segmentation
Ji, Y.; Zhang, R.; Wang, H.; Li, Z.; Wu, L.; Zhang, S.; and Luo, P. 2021 · 2021
Closest in time.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
Closest in time.
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
Valanarasu, J. M. J.; Oza, P.; Hacihaliloglu, I.; and Patel, V. M. 2021 · 2021
Closest in time.
A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation
Zhang, Y.; Higashita, R.; Fu, H.; Xu, Y.; Zhang, Y.; Liu, H.; Zhang, J.; and Liu, J. 2021 · 2021
Closest in time.
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Zhang, Y.; Liu, H.; and Hu, Q. 2021 · 2021
Closest in time.