A survey on incorporating domain knowledge into deep learning for medical image analysis
Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
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 …
Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN
J Sun, Y Peng, Y Guo, D Li - Neurocomputing, 2021 - Elsevier
Segmentation of multimodal brain tissues from 3D medical images is of great significance for
brain diagnosis. It is required to create an automated and accurate segmentation based on …
brain diagnosis. It is required to create an automated and accurate segmentation based on …
Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE
R Hashemzehi, SJS Mahdavi, M Kheirabadi… - biocybernetics and …, 2020 - Elsevier
A brain tumor is an abnormal growth of cells inside the skull. Malignant brain tumors are
among the most dreadful types of cancer with direct consequences such as cognitive …
among the most dreadful types of cancer with direct consequences such as cognitive …
[HTML][HTML] Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools
The vast amount of data produced by today's medical imaging systems has led medical
professionals to turn to novel technologies in order to efficiently handle their data and exploit …
professionals to turn to novel technologies in order to efficiently handle their data and exploit …
Attention to lesion: Lesion-aware convolutional neural network for retinal optical coherence tomography image classification
Automatic and accurate classification of retinal optical coherence tomography (OCT) images
is essential to assist ophthalmologist in the diagnosis and grading of macular diseases …
is essential to assist ophthalmologist in the diagnosis and grading of macular diseases …
[HTML][HTML] What is new in computer vision and artificial intelligence in medical image analysis applications
J Olveres, G González, F Torres… - … imaging in medicine …, 2021 - ncbi.nlm.nih.gov
Computer vision and artificial intelligence applications in medicine are becoming
increasingly important day by day, especially in the field of image technology. In this paper …
increasingly important day by day, especially in the field of image technology. In this paper …
[HTML][HTML] Role of artificial intelligence in MS clinical practice
R Bonacchi, M Filippi, MA Rocca - NeuroImage: Clinical, 2022 - Elsevier
Abstract Machine learning (ML) and its subset, deep learning (DL), are branches of artificial
intelligence (AI) showing promising findings in the medical field, especially when applied to …
intelligence (AI) showing promising findings in the medical field, especially when applied to …
Volumetric segmentation of brain regions from MRI scans using 3D convolutional neural networks
Automated brain segmentation is an active research domain due to the association of
various neurological disorders with different regions of the brain, to help medical …
various neurological disorders with different regions of the brain, to help medical …
Application of convolutional neural network in segmenting brain regions from MRI data
Extracting knowledge from digital images largely depends on how well the mining
algorithms can focus on specific regions of the image. In multimodality image analysis …
algorithms can focus on specific regions of the image. In multimodality image analysis …