A survey on deep learning: Algorithms, techniques, and applications
The field of machine learning is witnessing its golden era as deep learning slowly becomes
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …
Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state‐of‐art applications
H Seo, M Badiei Khuzani, V Vasudevan… - Medical …, 2020 - Wiley Online Library
In recent years, significant progress has been made in developing more accurate and
efficient machine learning algorithms for segmentation of medical and natural images. In this …
efficient machine learning algorithms for segmentation of medical and natural images. In this …
Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm
JH Lee, DH Kim, SN Jeong, SH Choi - Journal of dentistry, 2018 - Elsevier
Objectives Deep convolutional neural networks (CNNs) are a rapidly emerging new area of
medical research, and have yielded impressive results in diagnosis and prediction in the …
medical research, and have yielded impressive results in diagnosis and prediction in the …
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Abstract Google Earth Engine is a cloud-based platform for planetary-scale geospatial
analysis that brings Google's massive computational capabilities to bear on a variety of high …
analysis that brings Google's massive computational capabilities to bear on a variety of high …
metapath2vec: Scalable representation learning for heterogeneous networks
We study the problem of representation learning in heterogeneous networks. Its unique
challenges come from the existence of multiple types of nodes and links, which limit the …
challenges come from the existence of multiple types of nodes and links, which limit the …
[HTML][HTML] Weakly-supervised convolutional neural networks for multimodal image registration
One of the fundamental challenges in supervised learning for multimodal image registration
is the lack of ground-truth for voxel-level spatial correspondence. This work describes a …
is the lack of ground-truth for voxel-level spatial correspondence. This work describes a …
Cryptflow2: Practical 2-party secure inference
We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep
Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both …
Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both …
The challenge of machine learning in space weather: Nowcasting and forecasting
E Camporeale - Space weather, 2019 - Wiley Online Library
The numerous recent breakthroughs in machine learning make imperative to carefully
ponder how the scientific community can benefit from a technology that, although not …
ponder how the scientific community can benefit from a technology that, although not …
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
Single-cell RNA-sequencing has great potential to discover cell types, identify cell states,
trace development lineages, and reconstruct the spatial organization of cells. However …
trace development lineages, and reconstruct the spatial organization of cells. However …
Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm
JH Lee, D Kim, SN Jeong… - Journal of periodontal & …, 2018 - synapse.koreamed.org
Purpose The aim of the current study was to develop a computer-assisted detection system
based on a deep convolutional neural network (CNN) algorithm and to evaluate the …
based on a deep convolutional neural network (CNN) algorithm and to evaluate the …