A review on deep learning in medical image analysis
S Suganyadevi, V Seethalakshmi… - International Journal of …, 2022 - Springer
Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …
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 …
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
S Bhattacharya, PKR Maddikunta, QV Pham… - Sustainable cities and …, 2021 - Elsevier
Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many
death cases and affected all sectors of human life. With gradual progression of time, COVID …
death cases and affected all sectors of human life. With gradual progression of time, COVID …
An overview of deep learning in medical imaging focusing on MRI
AS Lundervold, A Lundervold - arxiv preprint arxiv:1811.10052, 2018 - arxiv.org
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 …
A brief survey on semantic segmentation with deep learning
S Hao, Y Zhou, Y Guo - Neurocomputing, 2020 - Elsevier
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
Transfer learning for medical images analyses: A survey
The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …
science and also revitalized numerous fields where traditional machine learning methods …
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 …
Cancer diagnosis using deep learning: a bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …
steps of cancer diagnosis followed by the typical classification methods used by doctors …
CT‐based automatic spine segmentation using patch‐based deep learning
CT vertebral segmentation plays an essential role in various clinical applications, such as
computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral …
computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral …
Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images
Segmentation of livers and liver tumors is one of the most important steps in radiation
therapy of hepatocellular carcinoma. The segmentation task is often done manually, making …
therapy of hepatocellular carcinoma. The segmentation task is often done manually, making …