Survey of deep learning in breast cancer image analysis
TG Debelee, F Schwenker, A Ibenthal, D Yohannes - Evolving Systems, 2020 - Springer
Computer-aided image analysis for better understanding of images has been time-honored
approaches in the medical computing field. In the conventional machine learning approach …
approaches in the medical computing field. In the conventional machine learning approach …
Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease
Alzheimer's disease (AD) is a major neurodegenerative disease and the most common
cause of dementia. Currently, no treatment exists to slow down or stop the progression of …
cause of dementia. Currently, no treatment exists to slow down or stop the progression of …
A survey on incomplete multiview clustering
Conventional multiview clustering seeks to partition data into respective groups based on
the assumption that all views are fully observed. However, in practical applications, such as …
the assumption that all views are fully observed. However, in practical applications, such as …
Landmark-based deep multi-instance learning for brain disease diagnosis
Abstract In conventional Magnetic Resonance (MR) image based methods, two stages are
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …
Joint classification and regression via deep multi-task multi-channel learning for Alzheimer's disease diagnosis
In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain
diseases and predicting clinical scores using magnetic resonance imaging (MRI) have …
diseases and predicting clinical scores using magnetic resonance imaging (MRI) have …
Deep learning framework for Alzheimer's disease diagnosis via 3D-CNN and FSBi-LSTM
Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild
cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a …
cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a …
Multi-modal deep learning model for auxiliary diagnosis of Alzheimer's disease
F Zhang, Z Li, B Zhang, H Du, B Wang, X Zhang - Neurocomputing, 2019 - Elsevier
Alzheimer's disease (AD) is one of the most difficult to cure diseases. Alzheimer's disease
seriously affects the normal lives of the elderly and their families. The mild cognitive …
seriously affects the normal lives of the elderly and their families. The mild cognitive …
Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer's disease diagnosis
Multi-modal neuroimages (eg, MRI and PET) have been widely used for diagnosis of brain
diseases such as Alzheimer's disease (AD) by providing complementary information …
diseases such as Alzheimer's disease (AD) by providing complementary information …
Latent representation learning for Alzheimer's disease diagnosis with incomplete multi-modality neuroimaging and genetic data
The fusion of complementary information contained in multi-modality data [eg, magnetic
resonance imaging (MRI), positron emission tomography (PET), and genetic data] has …
resonance imaging (MRI), positron emission tomography (PET), and genetic data] has …
Identifying autism spectrum disorder with multi-site fMRI via low-rank domain adaptation
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by a
wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early …
wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early …