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AUNet: Attention-guided dense-upsampling networks for breast mass
  segmentation in whole mammograms

AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

24 October 2018
Hui Sun
Cheng Li
Boqiang Liu
Hairong Zheng
David Dagan Feng
Shanshan Wang
ArXivPDFHTML

Papers citing "AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms"

4 / 4 papers shown
Title
Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation
Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation
Yiqi Chen
Ganghai Huang
Sheng Zhang
Jianglin Dai
25
0
0
13 May 2025
SWIN-SFTNet : Spatial Feature Expansion and Aggregation using Swin
  Transformer For Whole Breast micro-mass segmentation
SWIN-SFTNet : Spatial Feature Expansion and Aggregation using Swin Transformer For Whole Breast micro-mass segmentation
Sharif Amit Kamran
Khondker Fariha Hossain
Alireza Tavakkoli
G. Bebis
Salah A. Baker
ViT
9
4
0
16 Nov 2022
A Survey on Deep Learning in Medical Image Analysis
A Survey on Deep Learning in Medical Image Analysis
G. Litjens
Thijs Kooi
B. Bejnordi
A. Setio
F. Ciompi
Mohsen Ghafoorian
Jeroen van der Laak
Bram van Ginneken
C. I. Sánchez
OOD
278
10,608
0
19 Feb 2017
Real-Time Single Image and Video Super-Resolution Using an Efficient
  Sub-Pixel Convolutional Neural Network
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi
Jose Caballero
Ferenc Huszár
J. Totz
Andrew P. Aitken
Rob Bishop
Daniel Rueckert
Zehan Wang
SupR
190
5,163
0
16 Sep 2016
1