Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis
Early detection of COVID-19 based on chest CT enables timely treatment of patients and
helps control the spread of the disease. We proposed an artificial intelligence (AI) system for …
helps control the spread of the disease. We proposed an artificial intelligence (AI) system for …
Secure and robust machine learning for healthcare: A survey
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …
(DL) techniques due to their superior performance for a variety of healthcare applications …
[HTML][HTML] Attention gated networks: Learning to leverage salient regions in medical images
We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …
[HTML][HTML] Hybrid deep learning for detecting lung diseases from X-ray images
Lung disease is common throughout the world. These include chronic obstructive pulmonary
disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is …
disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is …
AnatomyNet: deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy
Purpose Radiation therapy (RT) is a common treatment option for head and neck (HaN)
cancer. An important step involved in RT planning is the delineation of organs‐at‐risks …
cancer. An important step involved in RT planning is the delineation of organs‐at‐risks …
Automated pulmonary nodule detection in CT images using deep convolutional neural networks
Lung cancer is one of the leading causes of cancer-related death worldwide. Early
diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an …
diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an …
A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning
Y Gu, J Chi, J Liu, L Yang, B Zhang, D Yu… - Computers in biology …, 2021 - Elsevier
Lung cancer has one of the highest mortalities of all cancers. According to the National Lung
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …
Deep learning techniques to diagnose lung cancer
L Wang - Cancers, 2022 - mdpi.com
Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …
research directions of deep learning techniques for lung cancer and pulmonary nodule …