Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Deep Learning-Enabled Technologies for Bioimage Analysis
Download
micromachines-13-00260-v2.pdf
Date
2022-02-01
Author
Rabbi, Fazle
Dabbagh, Sajjad Rahmani
Angın, Pelin
Yetisen, Ali Kemal
Tasoglu, Savas
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
242
views
98
downloads
Cite This
Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.
Subject Keywords
Bioimage quantification
,
Cancer diagnosis
,
Cell morphology classifica-tion
,
Deep learning
,
Machine learning
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124353297&origin=inward
https://hdl.handle.net/11511/97034
Journal
Micromachines
DOI
https://doi.org/10.3390/mi13020260
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Deep Learning-Based Hybrid Approach for Phase Retrieval
IŞIL, ÇAĞATAY; Öktem, Sevinç Figen; KOÇ, AYKUT (2019-06-24)
We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.
Multi-time-scale input approaches for hourly-scale rainfall-runoff modeling based on recurrent neural networks
Ishida, Kei; Kiyama, Masato; Ercan, Ali; Amagasaki, Motoki; Tu, Tongbi (2021-11-01)
This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, ...
Computational representation of protein sequences for homology detection and classification
Oğul, Hasan; Mumcuoğlu, Ünal Erkan; Department of Information Systems (2006)
Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...
HYPERSPECTRAL CLASSIFICATION USING STACKED AUTOENCODERS WITH DEEP LEARNING
Özdemir, Ataman; Cetin, C. Yasemin Yardimci (2014-06-27)
In this study, stacked autoencoders which are widely utilized in deep learning research are applied to remote sensing domain for hyperspectral classification. High dimensional hyperspectral data is an excellent candidate for deep learning methods. However, there are no works in literature that focuses on such deep learning approaches for hyperspectral imagery. This study aims to fill this gap by utilizing stacked autoencoders. Experiments are conducted on the Pavia University scene. Using stacked autoencode...
Mesh Learning for Object Classification using fMRI Measurements
Ekmekci, Ömer; Ozay, Mete; Oztekin, Ilke; GİLLAM, İLKE; Oztekin, Uygar (2013-09-18)
Machine learning algorithms have been widely used as reliable methods for modeling and classifying cognitive processes using functional Magnetic Resonance Imaging (fMRI) data. In this study, we aim to classify fMRI measurements recorded during an object recognition experiment. Previous studies focus on Multi Voxel Pattern Analysis (MVPA) which feeds a set of active voxels in a concatenated vector form to a machine learning algorithm to train and classify the cognitive processes. In most of the MVPA methods,...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
F. Rabbi, S. R. Dabbagh, P. Angın, A. K. Yetisen, and S. Tasoglu, “Deep Learning-Enabled Technologies for Bioimage Analysis,”
Micromachines
, vol. 13, no. 2, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124353297&origin=inward.