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Found 4 papers, 1 papers with code
MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis
In this work, we explore the concept of self-attention for tackling such subtleties in and between diseases.
COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images
As the COVID-19 pandemic continues to devastate globally, the use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow given its routine clinical use for respiratory complaint.
COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images
More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16, 000 CXR images from a multinational cohort of over 15, 000 patient cases into a custom network architecture for severity assessment.
TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Images
Motivated by this pressing need and the recent recommendation by the World Health Organization (WHO) for the use of computer-aided diagnosis of TB, we introduce TB-Net, a self-attention deep convolutional neural network tailored for TB case screening.
