Learning invariant representation for unsupervised domain adaptive thorax disease classification

Author:

Zhang Ruihua,Yang Fan,Luo Yan,Liu Jianyi,Wang Cong

Funder

NSFC

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference52 articles.

1. A review on medical image analysis with convolutional neural networks;Bir,2020

2. Deep learning applied to automatic disease detection using chest X-rays;Moses;J. Med. Imaging Radiat. Oncol.,2021

3. MHA-CoroCapsule: multi-head attention routing-based capsule network for COVID-19 chest X-ray image classification;Li;TMI,2021

4. Learning hierarchical attention for weakly-supervised chest X-ray abnormality localization and diagnosis;Ouyang;TMI,2021

5. Self-supervised deep convolutional neural network for chest X-ray classification;Gazda;IEEE Access,2021

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1. Learning to Generalize Towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection From Chest X-Rays;IEEE Journal of Biomedical and Health Informatics;2024-06

2. Loose to compact feature alignment for domain adaptive object detection;Pattern Recognition Letters;2024-05

3. A coincidence detection perspective for the maximum mean discrepancy;Pattern Recognition Letters;2024-01

4. Joined CNN and transformer network for thorax disease classification;Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023);2023-10-17

5. Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images;Frontiers in Artificial Intelligence;2023-02-09

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