Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation

30 September 2021

Papers citing "Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation"

4 / 4 papers shown

Title
Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation Bruno A. Krinski Daniel V. Ruiz E. Todt 3DPC 30 2 0 19 May 2022
Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation Yichi Zhang Qingcheng Liao Lin Yuan He Zhu Jiezhen Xing Jicong Zhang 44 23 0 31 Dec 2020
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 261 10,196 0 16 Nov 2016
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan Alex Kendall R. Cipolla SSeg 432 15,595 0 02 Nov 2015