Transfer learning techniques for medical image analysis: A review
Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
An overview of deep learning methods for multimodal medical data mining
Deep learning methods have achieved significant results in various fields. Due to the
success of these methods, many researchers have used deep learning algorithms in …
success of these methods, many researchers have used deep learning algorithms in …
A fully automated multimodal MRI-based multi-task learning for glioma segmentation and IDH genotyping
The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma
segmentation are important tasks for computer-aided diagnosis using preoperative …
segmentation are important tasks for computer-aided diagnosis using preoperative …
Brain tumor characterization using radiogenomics in artificial intelligence framework
Simple Summary Radiogenomics is a relatively new advancement in the understanding of
the biology and behaviour of cancer in response to conventional treatments. One of the most …
the biology and behaviour of cancer in response to conventional treatments. One of the most …
Radiomics in glioblastoma: current status and challenges facing clinical implementation
Radiomics analysis has had remarkable progress along with advances in medical imaging,
most notability in central nervous system malignancies. Radiomics refers to the extraction of …
most notability in central nervous system malignancies. Radiomics refers to the extraction of …
Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
A new pyramidal concatenated CNN approach for environmental sound classification
Recently, there has been an incremental interest on Environmental Sound Classification
(ESC), which is an important topic of the non-speech audio classification task. A novel …
(ESC), which is an important topic of the non-speech audio classification task. A novel …
Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning
Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse
and anaplastic astrocytic and oligodendroglial tumours as well as in secondary …
and anaplastic astrocytic and oligodendroglial tumours as well as in secondary …
Deep semi-supervised learning for brain tumor classification
Background This paper addresses issues of brain tumor, glioma, classification from four
modalities of Magnetic Resonance Image (MRI) scans (ie, T1 weighted MRI, T1 weighted …
modalities of Magnetic Resonance Image (MRI) scans (ie, T1 weighted MRI, T1 weighted …