A multi-scale context aware attention model for medical image segmentation
Medical image segmentation is critical for efficient diagnosis of diseases and treatment
planning. In recent years, convolutional neural networks (CNN)-based methods, particularly …
planning. In recent years, convolutional neural networks (CNN)-based methods, particularly …
Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation
Lung disease analysis in chest X-rays (CXR) using deep learning presents significant
challenges due to the wide variation in lung appearance caused by disease progression …
challenges due to the wide variation in lung appearance caused by disease progression …
Multi-task localization of the hemidiaphragms and lung segmentation in portable chest X-ray images of COVID-19 patients
Background The COVID-19 can cause long-term symptoms in the patients after they
overcome the disease. Given that this disease mainly damages the respiratory system, these …
overcome the disease. Given that this disease mainly damages the respiratory system, these …
Deep learning-based severity analysis of pneumoconiosis in Chest X-Rays
MS Alam - 2024 - unsworks.unsw.edu.au
The thesis objective is to develop a deep learning-based system for severity analysis of
pneumoconiosis, which would contribute to the monitoring and management of disease …
pneumoconiosis, which would contribute to the monitoring and management of disease …
[引用][C] Medical image segmentation using grey wolf-based u-net with bi-directional convolutional LSTM
G Tamilmani, CH Phaneendra Varma… - … Journal of Pattern …, 2024 - World Scientific
In recent years, deep learning-based networks have been able to achieve state-of-the-art
performance in medical image segmentation. U-Net, one of the currently available networks …
performance in medical image segmentation. U-Net, one of the currently available networks …