Fetching the paper…
Reading the bibliography…
In the field of medical image segmentation, variant models based on Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very widely developed and applied.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman · 1960
Earlier work this paper cites.
Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
Jorge Bernal, F Javier Sánchez, Gloria Fernández-Esparrach, Debora Gil, Cristina Rodríguez, and Fernando Vilariño · 2015
Earlier work this paper cites.
Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Bennett Landman, Zhoubing Xu, J Igelsias, Martin Styner, Thomas Langerak, and Arno Klein · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al · 2018
Earlier work this paper cites.
Attention u-net: Learning where to look for the pancreas
Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, et al · 2018
Earlier work this paper cites.
Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Earlier work this paper cites.
Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2021
Earlier work this paper cites.
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
Earlier work this paper cites.
A review of deep-learning-based medical image segmentation methods
Xiangbin Liu, Liping Song, Shuai Liu, and Yudong Zhang · 2021
Earlier work this paper cites.
Global filter networks for image classification
Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, and Jie Zhou · 2021
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Cited alongside, same era.
Precise yet efficient semantic calibration and refinement in convnets for real-time polyp segmentation from colonoscopy videos
Huisi Wu, Jiafu Zhong, Wei Wang, Zhenkun Wen, and Jing Qin · 2021
Cited alongside, same era.
Automatic polyp segmentation via multi-scale subtraction network
Xiaoqi Zhao, Lihe Zhang, and Huchuan Lu · 2021
Cited alongside, same era.
The medical segmentation decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M Summers, et al · 2022
Cited alongside, same era.
Devil is in channels: Contrastive single domain generalization for medical image segmentation
Shishuai Hu, Zehui Liao, and Yong Xia · 2023
Later among the works it cites.
A review on recent developments in cancer detection using machine learning and deep learning models
Sonam Maurya, Sushil Tiwari, Monika Chowdary Mothukuri, Chandra Mallika Tangeda, Rohitha Naga Sri Nandigam, and Durga Chandana Addagiri · 2023
Later among the works it cites.
U-net v2: Rethinking the skip connections of u-net for medical image segmentation
Yaopeng Peng, Milan Sonka, and Danny Z Chen · 2023
Later among the works it cites.
Meta-unet: Multi-scale efficient transformer attention unet for fast and high-accuracy polyp segmentation
Huisi Wu, Zebin Zhao, and Zhaoze Wang · 2023
Later among the works it cites.
Automatic skin lesion segmentation based on higher-order spatial interaction model
Renkai Wu, Pengchen Liang, Xuan Huang, Ziyuan Yang, Liu Shi, Yuandong Gu, Haiqin Zhu, and Qing Chang · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Transnorm: Transformer provides a strong spatial normalization mechanism for a deep segmentation model
Reza Azad, Mohammad T Al-Antary, Moein Heidari, and Dorit Merhof · 2022
Cited alongside, same era.
Swin-unet: Unet-like pure transformer for medical image segmentation
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang · 2022
Cited alongside, same era.
Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding · 2022
Cited alongside, same era.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Cited alongside, same era.
Hornet: Efficient high-order spatial interactions with recursive gated convolutions
Yongming Rao, Wenliang Zhao, Yansong Tang, Jie Zhou, Ser Nam Lim, and Jiwen Lu · 2022
Cited alongside, same era.
Malunet: A multi-attention and light-weight unet for skin lesion segmentation
Jiacheng Ruan, Suncheng Xiang, Mingye Xie, Ting Liu, and Yuzhuo Fu · 2022
Cited alongside, same era.
Attention swin u-net: Cross-contextual attention mechanism for skin lesion segmentation
Ehsan Khodapanah Aghdam, Reza Azad, Maral Zarvani, and Dorit Merhof · 2023
Cited alongside, same era.
Later among the works it cites.
Renkai Wu, Yinghao Liu, Pengchen Liang, and Qing Chang · 2023
Later among the works it cites.
Transformers in medical image segmentation: A review
Hanguang Xiao, Li Li, Qiyuan Liu, Xiuhong Zhu, and Qihang Zhang · 2023
Later among the works it cites.
Vmamba: Visual state space model
Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu · 2024
Closest in time.
Vm-unet: Vision mamba unet for medical image segmentation
Jiacheng Ruan and Suncheng Xiang · 2024
Closest in time.
Mhorunet: High-order spatial interaction unet for skin lesion segmentation
Renkai Wu, Pengchen Liang, Xuan Huang, Liu Shi, Yuandong Gu, Haiqin Zhu, and Qing Chang · 2024
Closest in time.
Hsh-unet: Hybrid selective high order interactive u-shaped model for automated skin lesion segmentation
Renkai Wu, Hongli Lv, Pengchen Liang, Xiaoxu Cui, Qing Chang, and Xuan Huang · 2024
Closest in time.
Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang · 2024
Closest in time.