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U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models.
A source-free domain adaptive polyp detection framework with style diversification flow
Liu, X.; and Yuan, Y. 2022 · 1908
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On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition
Kolmogorov, A. N. 1957 · 1957
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On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables
Kolmogorov, A. N. 1961 · 1961
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Multilayer feedforward networks are universal approximators
Hornik, K.; Stinchcombe, M.; and White, H. 1989 · 1989
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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Generative Adversarial Nets
Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A. C.; and Bengio, Y. 2014 · 2014
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Deep architecture of Kolmogorov-Arnold representation
Huang, G.-B.; Zhao, L.; and Song, Y. 2014 · 2014
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Conditional generative adversarial nets
Mirza, M.; and Osindero, S. 2014 · 2014
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WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
Bernal, J.; Sánchez, F. J.; Fernández-Esparrach, G.; Gil, D.; Rodríguez, C.; and Vilariño, F. 2015 · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data
Andermatt, S.; Pezold, S.; and Cattin, P. C. 2016 · 2016
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Ba, J. L.; Kiros, J. R.; and Hinton, G. E. 2016 · 2016
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3D U-Net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö.; Abdulkadir, A.; Lienkamp, S. S.; Brox, T.; and Ronneberger, O. 2016 · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F.; Navab, N.; and Ahmadi, S.-A. 2016 · 2016
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Towards theory of deep learning on graphs: Optimization landscape and train ability of Kolmogorov-Arnold representation
Huang, G.-B.; Zhao, L.; and Xing, Y. 2017 · 2017
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Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Kamnitsas, K.; Ledig, C.; Newcombe, V. F.; Simpson, J. P.; Kane, A. D.; Menon, D. K.; Rueckert, D.; and Glocker, B. 2017 · 2017
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Temporal generative adversarial nets with singular value clipping
Saito, M.; Matsumoto, E.; and Saito, S. 2017 · 2017
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Deep learning in medical image analysis
Shen, D.; Wu, G.; and Suk, H.-I. 2017 · 2017
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Neural discrete representation learning
Van Den Oord, A.; Vinyals, O.; et al. 2017 · 2017
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Large scale GAN training for high fidelity natural image synthesis
Brock, A.; Donahue, J.; and Simonyan, K. 2018 · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Karras, T.; Aila, T.; Laine, S.; and Lehtinen, J. 2018 · 2018
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Deep Kolmogorov-Arnold representation for learning dynamics
Liang, X.; Zhao, L.; and Huang, G.-B. 2018 · 2018
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Y-Net: joint segmentation and classification for diagnosis of breast biopsy images
Mehta, S.; Mercan, E.; Bartlett, J.; Weaver, D.; Elmore, J. G.; and Shapiro, L. 2018 · 2018
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3D MRI brain tumor segmentation using autoencoder regularization
Myronenko, A. 2019 · 2018
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Attention u-net: Learning where to look for the pancreas
Oktay, O.; Schlemper, J.; Folgoc, L. L.; Lee, M.; Heinrich, M.; Misawa, K.; Mori, K.; McDonagh, S.; Hammerla, N. Y.; Kainz, B.; et al. 2018 · 2018
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Kolmogorov-Arnold representation based deep learning for time series forecasting
Xing, Y.; Zhao, L.; and Huang, G.-B. 2018 · 2018
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Unet++: A Nested U-Net Architecture for Medical Image Segmentation
Zhou, Z.; Siddiquee, M. M. R.; Tajbakhsh, N.; and Liang, J. 2018 · 2018
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CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Gu, Z.; Cheng, J.; Fu, H.; Zhou, K.; Hao, H.; Zhao, Y.; Zhang, T.; Gao, S.; and Liu, J. 2019 · 2019
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Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
Schlemper, J.; Oktay, O.; Schaap, M.; Heinrich, M.; Kainz, B.; Glocker, B.; and Rueckert, D. 2019 · 2019
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Dataset of breast ultrasound images
Al-Dhabyani, W.; Gomaa, M.; Khaled, H.; and Fahmy, A. 2020 · 2020
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Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
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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.-L.; and Xu, W. 2020 · 2020
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Deep learning for spatio-temporal data mining: A survey
Wang, S.; Cao, J.; and Philip, S. Y. 2020 · 2020
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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
Cited alongside, same era.
Ilvr: Conditioning method for denoising diffusion probabilistic models
Choi, J.; Kim, S.; Jeong, Y.; Gwon, Y.; and Yoon, S. 2021 · 2021
Cited alongside, same era.
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.; et al. 2021 · 2021
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Esser, P.; Rombach, R.; and Ommer, B. 2021 · 2021
Cited alongside, same era.
Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Hatamizadeh, A.; Nath, V.; Tang, Y.; Yang, D.; Roth, H. R.; and Xu, D. 2021 · 2021
Hierarchical text-conditional image generation with CLIP latents
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
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Few-shot medical image segmentation using a global correlation network with discriminative embedding
Sun, L.; Li, C.; Ding, X.; Huang, Y.; Chen, Z.; Wang, G.; Yu, Y.; and Paisley, J. 2022 · 2022
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Unext: Mlp-based rapid medical image segmentation network
Valanarasu, J. M. J.; and Patel, V. M. 2022 · 2022
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AFSC: Adaptive Fourier Space Compression for Anomaly Detection
Xu, H.; Zhang, Y.; Sun, L.; Li, C.; Huang, Y.; and Ding, X. 2022 · 2022
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Cited alongside, same era.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F.; Jaeger, P. F.; Kohl, S. A.; Petersen, J.; and Maier-Hein, K. H. 2021 · 2021
Cited alongside, same era.
CT-based pelvic T1-weighted MR image synthesis using UNet, UNet++ and cycle-consistent generative adversarial network (Cycle-GAN)
Kalantar, R.; Messiou, C.; Winfield, J. M.; Renn, A.; Latifoltojar, A.; Downey, K.; Sohaib, A.; Lalondrelle, S.; Koh, D.-M.; and Blackledge, M. D. 2021 · 2021
Cited alongside, same era.
Consistent posterior distributions under vessel-mixing: a regularization for cross-domain retinal artery/vein classification
Li, C.; Zhang, Y.; Liang, Z.; Ma, W.; Huang, Y.; and Ding, X. 2021b · 2021
Cited alongside, same era.
Consolidated domain adaptive detection and localization framework for cross-device colonoscopic images
Liu, X.; Guo, X.; Liu, Y.; and Yuan, Y. 2021 · 2021
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B.; Srinivasan, P. P.; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; and Ng, R. 2021 · 2021
Cited alongside, same era.
On buggy resizing libraries and surprising subtleties in fid calculation
Parmar, G.; Zhang, R.; and Zhu, J.-Y. 2021 · 2021
Cited alongside, same era.
DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
Poirion, O. B.; Jing, Z.; Chaudhary, K.; Huang, S.; and Garmire, L. X. 2021 · 2021
Cited alongside, same era.
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Blattmann, A.; Rombach, R.; Ling, H.; Dockhorn, T.; Kim, S. W.; Fidler, S.; and Kreis, K. 2023 · 2023
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Medical federated learning with joint graph purification for noisy label learning
Chen, Z.; Li, W.; Xing, X.; and Yuan, Y. 2023 · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Gu, A.; and Dao, T. 2023 · 2023
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Global context vision transformers
Hatamizadeh, A.; Yin, H.; Heinrich, G.; Kautz, J.; and Molchanov, P. 2023 · 2023
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Steganerf: Embedding invisible information within neural radiance fields
Li, C.; Feng, B. Y.; Fan, Z.; Pan, P.; and Wang, Z. 2023 · 2023
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Novel Scenes & Classes: Towards Adaptive Open-set Object Detection
Li, W.; Guo, X.; and Yuan, Y. 2023 · 2023
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Decoupled Unbiased Teacher for Source-Free Domain Adaptive Medical Object Detection
Liu, X.; Li, W.; and Yuan, Y. 2023 · 2023
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Efficientvit: Memory efficient vision transformer with cascaded group attention
Liu, X.; Peng, H.; Zheng, N.; Yang, Y.; Hu, H.; and Yuan, Y. 2023 · 2023
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Pan, P.; Fan, Z.; Feng, B. Y.; Wang, P.; Li, C.; and Wang, Z. 2023 · 2023
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Scalable diffusion models with transformers
Peebles, W.; and Xie, S. 2023 · 2023
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Rwkv: Reinventing rnns for the transformer era
Peng, B.; Alcaide, E.; Anthony, Q.; Albalak, A.; Arcadinho, S.; Biderman, S.; Cao, H.; Cheng, X.; Chung, M.; Grella, M.; et al. 2023 · 2023
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Uvcgan: Unet vision transformer cycle-consistent gan for unpaired image-to-image translation
Torbunov, D.; Huang, Y.; Yu, H.; Huang, J.; Yoo, S.; Lin, M.; Viren, B.; and Ren, Y. 2023 · 2023
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MRM: Masked Relation Modeling for Medical Image Pre-Training with Genetics
Yang, Q.; Li, W.; Li, B.; and Yuan, Y. 2023 · 2023
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KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation
Cambrin, D. R.; Poeta, E.; Pastor, E.; Cerquitelli, T.; Baralis, E.; and Garza, P. 2024 · 2024
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TokenUnify: Scalable Autoregressive Visual Pre-training with Mixture Token Prediction
Chen, Y.; Shi, H.; Liu, X.; Shi, T.; Zhang, R.; Liu, D.; Xiong, Z.; and Wu, F. 2024 · 2024
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TKAN: Temporal Kolmogorov-Arnold Networks
Genet, R.; and Inzirillo, H. 2024 · 2024
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U-mamba: Enhancing long-range dependency for biomedical image segmentation
Ma, J.; Li, F.; and Wang, B. 2024 · 2024
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Moryossef, A. 2024 · 2024
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Masked Completion via Structured Diffusion with White-Box Transformers
Pai, D.; Buchanan, S.; Wu, Z.; Yu, Y.; and Ma, Y. 2024 · 2024
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VM-UNet: Vision Mamba UNet for Medical Image Segmentation
Ruan, J.; and Xiang, S. 2024 · 2024
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SeNMo: A self-normalizing deep learning model for enhanced multi-omics data analysis in oncology
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Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation
Xing, Z.; Ye, T.; Yang, Y.; Liu, G.; and Zhu, L. 2024 · 2024
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Immunotherapy efficacy prediction through a feature re-calibrated 2.5 D neural network
Xu, H.; Li, C.; Zhang, L.; Ding, Z.; Lu, T.; and Hu, H. 2024 · 2024
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UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation
Yang, R.; Chen, Y.; Zhang, Z.; Liu, X.; Li, Z.; He, K.; Xiong, Z.; Suo, J.; and Dai, Q. 2024 · 2024
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White-Box Transformers via Sparse Rate Reduction
Yu, Y.; Buchanan, S.; Pai, D.; Chu, T.; Wu, Z.; Tong, S.; Haeffele, B.; and Ma, Y. 2024 · 2024
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Vision mamba: Efficient visual representation learning with bidirectional state space model
Zhu, L.; Liao, B.; Zhang, Q.; Wang, X.; Liu, W.; and Wang, X. 2024 · 2024
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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Ali, S.; Ghatwary, N.; Jha, D.; Isik-Polat, E.; Polat, G.; Yang, C.; Li, W.; Galdran, A.; Ballester, M.-Á. G.; Thambawita, V.; et al. 2024 · 2032
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