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] An overview of deep learning in medical imaging focusing on MRI
AS Lundervold, A Lundervold - Zeitschrift für Medizinische Physik, 2019 - Elsevier
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
Convolutional neural networks are a promising tool for solving the problem of pattern
recognition. Most well-known convolutional neural networks implementations require a …
recognition. Most well-known convolutional neural networks implementations require a …
A decade survey of content based image retrieval using deep learning
SR Dubey - IEEE Transactions on Circuits and Systems for …, 2021 - ieeexplore.ieee.org
The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …
against a query image. Generally, the similarity between the representative features of the …
Medical image analysis using convolutional neural networks: a review
The science of solving clinical problems by analyzing images generated in clinical practice
is known as medical image analysis. The aim is to extract information in an affective and …
is known as medical image analysis. The aim is to extract information in an affective and …
[HTML][HTML] Explainable, trustworthy, and ethical machine learning for healthcare: A survey
With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …
for critical applications like healthcare, the questions about liability, trust, and interpretability …
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …
units are used to extract patterns from data in order to solve complex problems. Deep …
Optimal feature selection-based medical image classification using deep learning model in internet of medical things
Internet of Medical Things (IoMT) is the collection of medical devices and related
applications which link the healthcare IT systems through online computer networks. In the …
applications which link the healthcare IT systems through online computer networks. In the …
Survey on deep learning for radiotherapy
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in
combination with other methods. The planning and delivery of radiotherapy treatment is a …
combination with other methods. The planning and delivery of radiotherapy treatment is a …
[HTML][HTML] Deep convolutional neural networks for mammography: advances, challenges and applications
D Abdelhafiz, C Yang, R Ammar, S Nabavi - BMC bioinformatics, 2019 - Springer
Background The limitations of traditional computer-aided detection (CAD) systems for
mammography, the extreme importance of early detection of breast cancer and the high …
mammography, the extreme importance of early detection of breast cancer and the high …