U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …
tasks. These traits provide U-net with a high utility within the medical imaging community …
Medical image segmentation review: The success of u-net
Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
A hybrid deep learning-based approach for brain tumor classification
Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of
people die due to deadly brain tumors. Therefore, accurate detection and classification are …
people die due to deadly brain tumors. Therefore, accurate detection and classification are …
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 …
Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images
Gliomas are the most common malignant brain tumors with different grades that highly
determine the rate of survival in patients. Tumor segmentation and grading using magnetic …
determine the rate of survival in patients. Tumor segmentation and grading using magnetic …
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which
are associated with shape features. In this study, we propose a fully automatic way to …
are associated with shape features. In this study, we propose a fully automatic way to …
A survey on the new generation of deep learning in image processing
L Jiao, J Zhao - Ieee Access, 2019 - ieeexplore.ieee.org
During the past decade, deep learning is one of the essential breakthroughs made in
artificial intelligence. In particular, it has achieved great success in image processing …
artificial intelligence. In particular, it has achieved great success in image processing …
Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer
We apply for the first-time interpretable deep learning methods simultaneously to the most
common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal …
common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal …
DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex
morphological changes with the development and progression of glaucoma, their …
morphological changes with the development and progression of glaucoma, their …
Classification framework for medical diagnosis of brain tumor with an effective hybrid transfer learning model
Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the
world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require …
world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require …