Segmentation Ability Map: Interpret deep features for medical image segmentation

19 December 2022

Papers citing "Segmentation Ability Map: Interpret deep features for medical image segmentation"

8 / 8 papers shown

Title
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation Ran Gu Guotai Wang Tao Song Rui Huang Michael Aertsen Jan Deprest Sébastien Ourselin Tom Vercauteren Shaoting Zhang SSeg 84 461 0 22 Sep 2020
Concept Whitening for Interpretable Image Recognition Zhi Chen Yijie Bei Cynthia Rudin FAtt 58 317 0 05 Feb 2020
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation Zongwei Zhou M. R. Siddiquee Nima Tajbakhsh Jianming Liang SSeg 90 2,604 0 11 Dec 2019
A large annotated medical image dataset for the development and evaluation of segmentation algorithms Amber L. Simpson Michela Antonelli Spyridon Bakas Michel Bilello Keyvan Farahani ... M. McHugo S. Napel Eugene Vorontsov Lena Maier-Hein M. Jorge Cardoso 78 850 0 25 Feb 2019
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images Jo Schlemper Ozan Oktay M. Schaap M. Heinrich Bernhard Kainz Ben Glocker Daniel Rueckert MedIm 51 1,459 0 22 Aug 2018
Interpreting Deep Visual Representations via Network Dissection Bolei Zhou David Bau A. Oliva Antonio Torralba FAtt MILM 50 324 0 15 Nov 2017
To prune, or not to prune: exploring the efficacy of pruning for model compression Michael Zhu Suyog Gupta 132 1,262 0 05 Oct 2017
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 163 9,280 0 14 Dec 2015