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Auto-Generating Weak Labels for Real & Synthetic Data to Improve
  Label-Scarce Medical Image Segmentation

Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation

25 April 2024
Tanvi Deshpande
Eva Prakash
E. Ross
C. Langlotz
Andrew Ng
Jeya Maria Jose Valanarasu
    MedIm
    SyDa
    VLM
ArXivPDFHTML

Papers citing "Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation"

3 / 3 papers shown
Title
U-Mamba: Enhancing Long-range Dependency for Biomedical Image
  Segmentation
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Jun Ma
Feifei Li
Bo Wang
Mamba
77
327
0
09 Jan 2024
Zero-shot performance of the Segment Anything Model (SAM) in 2D medical
  imaging: A comprehensive evaluation and practical guidelines
Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines
C. M. Oliveira
L. V. Moura
R. Ravazio
L. S. Kupssinskü
Otávio Parraga
Marcelo Mussi Delucis
Rodrigo C. Barros
VLM
MedIm
70
25
0
28 Apr 2023
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
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