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Current state-of-the-art medical image segmentation methods prioritize accuracy but often at the expense of increased computational demands and larger model sizes.
Fully convolutional networks for semantic segmentation,
J. Long, E. Shelhamer, T. Darrell, · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation,
O. Ronneberger, P. Fischer, T. Brox, · 2015
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions,
F. Yu, V. Koltun, · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge,
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al., · 2015
Earlier work this paper cites.
3d u-net: learning dense volumetric segmentation from sparse annotation,
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger, · 2016
Earlier work this paper cites.
V-net: Fully convolutional neural networks for volumetric medical image segmentation,
F. Milletari, N. Navab, S.-A. Ahmadi, · 2016
Earlier work this paper cites.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size,
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, K. Keutzer, · 2016
Earlier work this paper cites.
Pyramid scene parsing network,
H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, · 2017
Earlier work this paper cites.
Inception-v4, inception-resnet and the impact of residual connections on learning,
C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi, · 2017
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions,
F. Chollet, · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks,
S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He, · 2017
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout,
T. DeVries, G. W. Taylor, · 2017
Earlier work this paper cites.
Automatic 3d cardiovascular mr segmentation with densely-connected volumetric convnets,
L. Yu, J.-Z. Cheng, Q. Dou, X. Yang, H. Chen, J. Qin, P.-A. Heng, · 2017
Earlier work this paper cites.
Unet++: A nested u-net architecture for medical image segmentation,
Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, J. Liang, · 2018
Earlier work this paper cites.
M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, V. K. Asari, · 2018
Earlier work this paper cites.
Attention u-net: Learning where to look for the pancreas,
O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz, et al., · 2018
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation,
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks,
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, · 2018
Earlier work this paper cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices,
X. Zhang, X. Zhou, M. Lin, J. Sun, · 2018
Earlier work this paper cites.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,
P. Tschandl, C. Rosendahl, H. Kittler, · 2018
Earlier work this paper cites.
Automatic multi-organ segmentation on abdominal ct with dense v-networks,
E. Gibson, F. Giganti, Y. Hu, E. Bonmati, S. Bandula, K. Gurusamy, B. Davidson, S. P. Pereira, M. J. Clarkson, D. C. Barratt, · 2018
Cited alongside, same era.
Local relation networks for image recognition,
H. Hu, Z. Zhang, Z. Xie, S. Lin, · 2019
Cited alongside, same era.
Stand-alone self-attention in vision models,
P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, J. Shlens, · 2019
Cited alongside, same era.
Ce-net: Context encoder network for 2d medical image segmentation,
Z. Gu, J. Cheng, H. Fu, K. Zhou, H. Hao, Y. Zhao, T. Zhang, S. Gao, J. Liu, · 2019
Cited alongside, same era.
Feature fusion encoder decoder network for automatic liver lesion segmentation,
X. Chen, R. Zhang, P. Yan, · 2019
Cited alongside, same era.
Focusnet: An attention-based fully convolutional network for medical image segmentation,
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,
F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, K. H. Maier-Hein, · 2021
Later among the works it cites.
Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,
Y. Xie, J. Zhang, C. Shen, Y. Xia, · 2021
Later among the works it cites.
Polarized self-attention: Towards high-quality pixel-wise regression,
H. Liu, F. Liu, X. Fan, D. Huang, · 2021
Later among the works it cites.
Segformer: Simple and efficient design for semantic segmentation with transformers,
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, P. Luo, · 2021
Later among the works it cites.
Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation,
C. Yu, C. Gao, J. Wang, G. Yu, C. Shen, N. Sang, · 2021
Later among the works it cites.
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C. Kaul, S. Manandhar, N. Pears, · 2019
Cited alongside, same era.
N. Codella, V. Rotemberg, P. Tschandl, M. E. Celebi, S. Dusza, D. Gutman, B. Helba, A. Kalloo, K. Liopyris, M. Marchetti, et al., · 2019
Cited alongside, same era.
Dual attention network for scene segmentation,
J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, H. Lu, · 2019
Cited alongside, same era.
Bi-directional convlstm u-net with densley connected convolutions,
R. Azad, M. Asadi-Aghbolaghi, M. Fathy, S. Escalera, · 2019
Cited alongside, same era.
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head ct using deep learning: an algorithm development and multicentre validation study,
M. Monteiro, V. F. Newcombe, F. Mathieu, K. Adatia, K. Kamnitsas, E. Ferrante, T. Das, D. Whitehouse, D. Rueckert, D. K. Menon, et al., · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale,
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., · 2020
Cited alongside, same era.
Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality mri,
L. Liu, S. Chen, F. Zhang, F.-X. Wu, Y. Pan, J. Wang, · 2020
Cited alongside, same era.
Cascade knowledge diffusion network for skin lesion diagnosis and segmentation,
Q. Jin, H. Cui, C. Sun, Z. Meng, R. Su, · 2021
Later among the works it cites.
Contnet: Why not use convolution and transformer at the same time?,
H. Yan, Z. Li, W. Li, C. Wang, M. Wu, C. Zhang, · 2021
Later among the works it cites.
Unetr: Transformers for 3d medical image segmentation,
A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H. R. Roth, D. Xu, · 2022
Later among the works it cites.
Swin-unet: Unet-like pure transformer for medical image segmentation,
H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, M. Wang, · 2022
Later among the works it cites.
Swinbts: A method for 3d multimodal brain tumor segmentation using swin transformer,
Y. Jiang, Y. Zhang, X. Lin, J. Dong, T. Cheng, J. Liang, · 2022
Later among the works it cites.
Q. Zhao, S. Lyu, W. Bai, L. Cai, B. Liu, M. Wu, X. Sang, M. Yang, L. Chen, · 2022
Later among the works it cites.
H. H. Lee, S. Bao, Y. Huo, B. A. Landman, · 2022
Later among the works it cites.
A. Trockman, J. Z. Kolter, · 2022
Later among the works it cites.
nnformer: volumetric medical image segmentation via a 3d transformer,
H.-Y. Zhou, J. Guo, Y. Zhang, X. Han, L. Yu, L. Wang, Y. Yu, · 2023
Later among the works it cites.
Transformers in medical imaging: A survey,
F. Shamshad, S. Khan, S. W. Zamir, M. H. Khan, M. Hayat, F. S. Khan, H. Fu, · 2023
Later among the works it cites.
Convnets match vision transformers at scale,
S. L. Smith, A. Brock, L. Berrada, S. De, · 2023
Later among the works it cites.
Cfatransunet: Channel-wise cross fusion attention and transformer for 2d medical image segmentation,
C. Wang, L. Wang, N. Wang, X. Wei, T. Feng, M. Wu, Q. Yao, R. Zhang, · 2024
Closest in time.
Leanet: Lightweight u-shaped architecture for high-performance skin cancer image segmentation,
B. Hu, P. Zhou, H. Yu, Y. Dai, M. Wang, S. Tan, Y. Sun, · 2024
Closest in time.
Lm-net: A light-weight and multi-scale network for medical image segmentation,
Z. Lu, C. She, W. Wang, Q. Huang, · 2024
Closest in time.
A fully automatic ai system for tooth and alveolar bone segmentation from cone-beam ct images,
Z. Cui, Y. Fang, L. Mei, B. Zhang, B. Yu, J. Liu, C. Jiang, Y. Sun, L. Ma, J. Huang, et al., · 2096
Closest in time.