Boosting Automatic COVID-19 Detection Performance with Self-Supervised
Learning and Batch Knowledge Ensembling

Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling

19 December 2022

Guang Li

Ren Togo

Takahiro Ogawa

Papers citing "Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling"

7 / 7 papers shown

Title
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representation from X-Ray Images Guang Li Ren Togo Takahiro Ogawa Miki Haseyama 10 0 0 01 Nov 2022
Dataset Distillation Using Parameter Pruning Guang Li Ren Togo Takahiro Ogawa Miki Haseyama DD 31 20 0 29 Sep 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,412 0 11 Nov 2021
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer Sachin Mehta Mohammad Rastegari ViT 189 1,200 0 05 Oct 2021
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review A. Shoeibi Marjane Khodatars M. Jafari Navid Ghassemi Delaram Sadeghi ... Z. Sani F. Khozeimeh S. Nahavandi U. Acharya Juan M Gorriz 33 176 0 16 Jul 2020
Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks A. Narin Ceren Kaya Ziynet Pamuk 67 1,759 0 24 Mar 2020
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 247 36,237 0 25 Aug 2016