Recent advances and clinical applications of deep learning in medical image analysis

X Chen, X Wang, K Zhang, KM Fung, TC Thai… - Medical image …, 2022 - Elsevier
Deep learning has received extensive research interest in develo** new medical image
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

I Qureshi, J Yan, Q Abbas, K Shaheed, AB Riaz… - Information …, 2023 - Elsevier
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 …

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

C **, W Chen, Y Cao, Z Xu, Z Tan, X Zhang… - Nature …, 2020 - nature.com
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 …

Secure and robust machine learning for healthcare: A survey

A Qayyum, J Qadir, M Bilal… - IEEE Reviews in …, 2020 - ieeexplore.ieee.org
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 …

[HTML][HTML] Attention gated networks: Learning to leverage salient regions in medical images

J Schlemper, O Oktay, M Schaap, M Heinrich… - Medical image …, 2019 - Elsevier
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 …

[HTML][HTML] Hybrid deep learning for detecting lung diseases from X-ray images

S Bharati, P Podder, MRH Mondal - Informatics in Medicine Unlocked, 2020 - Elsevier
Lung disease is common throughout the world. These include chronic obstructive pulmonary
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

W Zhu, Y Huang, L Zeng, X Chen, Y Liu, Z Qian… - Medical …, 2019 - Wiley Online Library
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 …

Automated pulmonary nodule detection in CT images using deep convolutional neural networks

H **e, D Yang, N Sun, Z Chen, Y Zhang - Pattern recognition, 2019 - Elsevier
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 …

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 …

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 …