Dense convolutional network and its application in medical image analysis
T Zhou, XY Ye, HL Lu, X Zheng, S Qiu… - BioMed Research …, 2022 - Wiley Online Library
Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent
years, which has good applications in medical image analysis. In this paper, DenseNet is …
years, which has good applications in medical image analysis. In this paper, DenseNet is …
Multi-site infant brain segmentation algorithms: the iSeg-2019 challenge
To better understand early brain development in health and disorder, it is critical to
accurately segment infant brain magnetic resonance (MR) images into white matter (WM) …
accurately segment infant brain magnetic resonance (MR) images into white matter (WM) …
Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region
G Karayegen, MF Aksahin - Biomedical Signal Processing and Control, 2021 - Elsevier
When it comes to medical image segmentation on brain MR images, using deep learning
techniques has a significant impact to predict tumor existence. Manual segmentation of a …
techniques has a significant impact to predict tumor existence. Manual segmentation of a …
A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images
Several techniques are used to detect brain tumors in the medical research field; however,
Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts …
Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts …
Self-supervised learning with application for infant cerebellum segmentation and analysis
Accurate tissue segmentation is critical to characterize early cerebellar development in the
first two postnatal years. However, challenges in tissue segmentation arising from tightly …
first two postnatal years. However, challenges in tissue segmentation arising from tightly …
A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images
Glioma is a relatively common brain tumor disease with high mortality rate. Humans have
been seeking a more effective therapy. In the course of treatment, the specific location of the …
been seeking a more effective therapy. In the course of treatment, the specific location of the …
DA-DSUnet: dual attention-based dense SU-net for automatic head-and-neck tumor segmentation in MRI images
Precise and accurate segmentation of the most common head-and-neck tumor,
nasopharyngeal carcinoma (NPC), in magnetic resonance images (MRI) sheds light on …
nasopharyngeal carcinoma (NPC), in magnetic resonance images (MRI) sheds light on …
A 3D cross-modality feature interaction network with volumetric feature alignment for brain tumor and tissue segmentation
Y Zhuang, H Liu, E Song… - IEEE Journal of Biomedical …, 2022 - ieeexplore.ieee.org
Accurate volumetric segmentation of brain tumors and tissues is beneficial for quantitative
brain analysis and brain disease identification in multi-modal Magnetic Resonance (MR) …
brain analysis and brain disease identification in multi-modal Magnetic Resonance (MR) …
Multimodal infant brain segmentation by fuzzy-informed deep learning
W Ding, M Abdel-Basset, H Hawash… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Magnetic resonance imaging (MRI) is a prevailing method of modal infant brain tissue
analysis that precisely segments brain tissue and is vitally important for diagnosis …
analysis that precisely segments brain tissue and is vitally important for diagnosis …
3d-ucaps: 3d capsules unet for volumetric image segmentation
Medical image segmentation has been so far achieving promising results with Convolutional
Neural Networks (CNNs). However, it is arguable that in traditional CNNs, its pooling layer …
Neural Networks (CNNs). However, it is arguable that in traditional CNNs, its pooling layer …