Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder

4 October 2018

Papers citing "Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder"

6 / 6 papers shown

Title
Medical Image Segmentation Review: The success of U-Net Reza Azad Ehsan Khodapanah Aghdam Amelie Rauland Yiwei Jia Atlas Haddadi Avval Afshin Bozorgpour Sanaz Karimijafarbigloo Joseph Paul Cohen Ehsan Adeli Dorit Merhof SSeg 22 265 0 27 Nov 2022
Modality specific U-Net variants for biomedical image segmentation: A survey Narinder Singh Punn Sonali Agarwal SSeg 29 144 0 09 Jul 2021
An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease Abbas Raza Ali M. Budka 27 5 0 29 Apr 2021
U-Net and its variants for medical image segmentation: theory and applications N. Siddique Sidike Paheding Colin P. Elkin Vijay Devabhaktuni SSeg 23 1,041 0 02 Nov 2020
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders Agostina J. Larrazabal Cesar E. Martínez Ben Glocker Enzo Ferrante 27 65 0 24 Jun 2020
BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset A. Signoroni Mattia Savardi Sergio Benini Nicola Adami R. Leonardi ... F. Vaccher M. Ravanelli A. Borghesi R. Maroldi D. Farina 16 37 0 08 Jun 2020