Siamese neural networks: An overview
D Chicco - Artificial neural networks, 2021 - Springer
Similarity has always been a key aspect in computer science and statistics. Any time two
element vectors are compared, many different similarity approaches can be used …
element vectors are compared, many different similarity approaches can be used …
Convolutional neural networks for radiologic images: a radiologist's guide
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …
recently gained particular attention in the radiology community. This article provides an …
Classification of brain tumor from magnetic resonance imaging using vision transformers ensembling
The automated classification of brain tumors plays an important role in supporting
radiologists in decision making. Recently, vision transformer (ViT)-based deep neural …
radiologists in decision making. Recently, vision transformer (ViT)-based deep neural …
Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy
G Urban, P Tripathi, T Alkayali, M Mittal, F Jalali… - Gastroenterology, 2018 - Elsevier
Background & Aims The benefit of colonoscopy for colorectal cancer prevention depends on
the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate …
the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate …
Meta-learning approaches for learning-to-learn in deep learning: A survey
Y Tian, X Zhao, W Huang - Neurocomputing, 2022 - Elsevier
Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …
representation and understand scattered data properties. It has gained considerable …
Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT
PD Chang, E Kuoy, J Grinband… - American Journal …, 2018 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: Convolutional neural networks are a powerful technology
for image recognition. This study evaluates a convolutional neural network optimized for the …
for image recognition. This study evaluates a convolutional neural network optimized for the …
[图书][B] Deep learning in science
P Baldi - 2021 - books.google.com
This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with
the foundations of the theory and building it up, this is essential reading for any scientists …
the foundations of the theory and building it up, this is essential reading for any scientists …
Machine learning in orthopedics: a literature review
In this paper we present the findings of a systematic literature review covering the articles
published in the last two decades in which the authors described the application of a …
published in the last two decades in which the authors described the application of a …
[HTML][HTML] Deep learning reveals cancer metastasis and therapeutic antibody targeting in the entire body
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting
therapeutic antibodies within the entire body has long been needed to better understand …
therapeutic antibodies within the entire body has long been needed to better understand …
Four-way classification of Alzheimer's disease using deep Siamese convolutional neural network with triplet-loss function
Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage
to several brain regions, including the hippocampus causing impairment in cognition …
to several brain regions, including the hippocampus causing impairment in cognition …