A survey of deep meta-learning
Deep neural networks can achieve great successes when presented with large data sets
and sufficient computational resources. However, their ability to learn new concepts quickly …
and sufficient computational resources. However, their ability to learn new concepts quickly …
Multimodal data integration for oncology in the era of deep neural networks: a review
Cancer research encompasses data across various scales, modalities, and resolutions, from
screening and diagnostic imaging to digitized histopathology slides to various types of …
screening and diagnostic imaging to digitized histopathology slides to various types of …
Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art
Abstract Analysis of skin lesion images via visual inspection and manual examination to
diagnose skin cancer has always been cumbersome. This manual examination of skin …
diagnose skin cancer has always been cumbersome. This manual examination of skin …
A novel machine-learning-based hybrid CNN model for tumor identification in medical image processing
The popularization of electronic clinical medical records makes it possible to use automated
methods to extract high-value information from medical records quickly. As essential medical …
methods to extract high-value information from medical records quickly. As essential medical …
A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data
T Liu, J Huang, T Liao, R Pu, S Liu, Y Peng - Irbm, 2022 - Elsevier
Background The prediction of breast cancer subtypes plays a key role in the diagnosis and
prognosis of breast cancer. In recent years, deep learning (DL) has shown good …
prognosis of breast cancer. In recent years, deep learning (DL) has shown good …
Deep learning recognition of diseased and normal cell representation
Cell classification refers to detecting normal and diseased cells from small amount of data.
Sometimes, classification of cells becomes difficult because some cells fall into more than …
Sometimes, classification of cells becomes difficult because some cells fall into more than …
Recognition of mRNA N4 acetylcytidine (ac4C) by using non-deep vs. Deep learning
Deep learning models have been successfully applied in a wide range of fields. The
creation of a deep learning framework for analyzing high-performance sequence data have …
creation of a deep learning framework for analyzing high-performance sequence data have …
Temporal and spatial analysis of alzheimer's disease based on an improved convolutional neural network and a resting-state FMRI brain functional network
H Sun, A Wang, S He - … Journal of Environmental Research and Public …, 2022 - mdpi.com
Most current research on Alzheimer's disease (AD) is based on transverse measurements.
Given the nature of neurodegeneration in AD progression, observing longitudinal changes …
Given the nature of neurodegeneration in AD progression, observing longitudinal changes …
Towards a universal mechanism for successful deep learning
Recently, the underlying mechanism for successful deep learning (DL) was presented
based on a quantitative method that measures the quality of a single filter in each layer of a …
based on a quantitative method that measures the quality of a single filter in each layer of a …
Machine learning based classification of mitochondrial morphologies from fluorescence microscopy images of Toxoplasma gondii cysts
BC Place, CA Troublefield, RD Murphy, AP Sinai… - PLoS …, 2023 - journals.plos.org
The mitochondrion is intimately linked to energy and overall metabolism and therefore the
morphology of mitochondrion can be very informative for inferring the metabolic state of …
morphology of mitochondrion can be very informative for inferring the metabolic state of …