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Self Pre-training with Topology- and Spatiality-aware Masked
  Autoencoders for 3D Medical Image Segmentation

Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation

15 June 2024
Pengfei Gu
Yejia Zhang
Huimin Li
Chaoli Wang
D. Z. Chen
    MedIm
ArXivPDFHTML

Papers citing "Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation"

5 / 5 papers shown
Title
Revisiting MAE pre-training for 3D medical image segmentation
Revisiting MAE pre-training for 3D medical image segmentation
Tassilo Wald
Constantin Ulrich
Stanislav Lukyanenko
Andrei Goncharov
Alberto Paderno
Leander Maerkisch
Paul F. Jäger
Paul F. Jäger
Klaus Maier-Hein
37
2
0
30 Oct 2024
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
258
7,412
0
11 Nov 2021
MISSFormer: An Effective Medical Image Segmentation Transformer
MISSFormer: An Effective Medical Image Segmentation Transformer
Xiaohong Huang
Zhifang Deng
Dandan Li
Xueguang Yuan
ViT
MedIm
87
172
0
15 Sep 2021
Convolution-Free Medical Image Segmentation using Transformers
Convolution-Free Medical Image Segmentation using Transformers
Davood Karimi
Serge Vasylechko
Ali Gholipour
ViT
MedIm
84
121
0
26 Feb 2021
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
Philipp Fischer
Thomas Brox
SSeg
3DV
232
75,445
0
18 May 2015
1