[HTML][HTML] A review of deep learning-based information fusion techniques for multimodal medical image classification
Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it
combines information from various imaging modalities to provide a more comprehensive …
combines information from various imaging modalities to provide a more comprehensive …
[HTML][HTML] A review of explainable deep learning cancer detection models in medical imaging
Deep learning has demonstrated remarkable accuracy analyzing images for cancer
detection tasks in recent years. The accuracy that has been achieved rivals radiologists and …
detection tasks in recent years. The accuracy that has been achieved rivals radiologists and …
Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives
Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
MS-Net: multi-site network for improving prostate segmentation with heterogeneous MRI data
Automated prostate segmentation in MRI is highly demanded for computer-assisted
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …
USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Prostate cancer is the most common malignant tumors in men but prostate Magnetic
Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland …
Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland …
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
Multiparametric magnetic resonance imaging (mp-MRI) has shown excellent results in the
detection of prostate cancer (PCa). However, characterizing prostate lesions …
detection of prostate cancer (PCa). However, characterizing prostate lesions …
Learning temporal and spatial correlations jointly: A unified framework for wind speed prediction
Leveraging both temporal and spatial correlations to predict wind speed remains one of the
most challenging and less studied areas of wind speed prediction. In this paper, the problem …
most challenging and less studied areas of wind speed prediction. In this paper, the problem …
[HTML][HTML] Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview
J Gao, Q Jiang, B Zhou, D Chen - Mathematical Biosciences and …, 2019 - aimspress.com
Computer-aided detection or diagnosis (CAD) has been a promising area of research over
the last two decades. Medical image analysis aims to provide a more efficient diagnostic and …
the last two decades. Medical image analysis aims to provide a more efficient diagnostic and …
Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI
Background The purpose of this study is to investigate the use of radiomics and deep
features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading …
features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading …
Joint prostate cancer detection and Gleason score prediction in mp-MRI via FocalNet
Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for
diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagnosis is currently limited …
diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagnosis is currently limited …