[HTML][HTML] Convolutional neural networks in medical image understanding: a survey
DR Sarvamangala, RV Kulkarni - Evolutionary intelligence, 2022 - Springer
Imaging techniques are used to capture anomalies of the human body. The captured images
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline
G Raghu, M Remy-Jardin, L Richeldi… - American Journal of …, 2022 - atsjournals.org
Background: This American Thoracic Society, European Respiratory Society, Japanese
Respiratory Society, and Asociación Latinoamericana de Tórax guideline updates prior …
Respiratory Society, and Asociación Latinoamericana de Tórax guideline updates prior …
[HTML][HTML] Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
A Abbas, MM Abdelsamea, MM Gaber - Applied Intelligence, 2021 - Springer
Chest X-ray is the first imaging technique that plays an important role in the diagnosis of
COVID-19 disease. Due to the high availability of large-scale annotated image datasets …
COVID-19 disease. Due to the high availability of large-scale annotated image datasets …
Brain tumor classification for MR images using transfer learning and fine-tuning
Accurate and precise brain tumor MR images classification plays important role in clinical
diagnosis and decision making for patient treatment. The key challenge in MR images …
diagnosis and decision making for patient treatment. The key challenge in MR images …
Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images
Advances in artificial intelligence technologies have made it possible to obtain more
accurate and reliable results using digital images. Due to the advances in digital …
accurate and reliable results using digital images. Due to the advances in digital …
A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …
Deep learning in medical image analysis
This review covers computer-assisted analysis of images in the field of medical imaging.
Recent advances in machine learning, especially with regard to deep learning, are helping …
Recent advances in machine learning, especially with regard to deep learning, are helping …
Convolutional neural networks for medical image analysis: Full training or fine tuning?
N Tajbakhsh, JY Shin, SR Gurudu… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
Training a deep convolutional neural network (CNN) from scratch is difficult because it
requires a large amount of labeled training data and a great deal of expertise to ensure …
requires a large amount of labeled training data and a great deal of expertise to ensure …
Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning
Remarkable progress has been made in image recognition, primarily due to the availability
of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs …
of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs …