Next Article in Journal
A Novel Intermittent Jumping Coupled Map Lattice Based on Multiple Chaotic Maps
Next Article in Special Issue
A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
Previous Article in Journal
On the Use of PEDOT as a Catalytic Counter Electrode Material in Dye-Sensitized Solar Cells
Previous Article in Special Issue
Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
 
 
Article

Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset

1
Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece
2
Medical School, University of Crete, 71003 Heraklion, Greece
3
School of Electrical & Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece
4
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
5
Department of Pathology, University Hospital of Crete, 71110 Heraklion, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Cecilia Di Ruberto, Andrea Loddo and Lorenzo Putzu
Appl. Sci. 2021, 11(9), 3796; https://doi.org/10.3390/app11093796
Received: 31 March 2021 / Revised: 18 April 2021 / Accepted: 20 April 2021 / Published: 22 April 2021
(This article belongs to the Special Issue Computer Aided Diagnosis)
Automated pathology image classification through modern machine learning (ML) techniques in quantitative microscopy is an emerging AI application area aiming to alleviate the increased workload of pathologists and improve diagnostic accuracy and consistency. However, there are very few efforts focusing on fluorescence histology image data, which is a challenging task, not least due to the variable imaging acquisition parameters in pooled data, which can diminish the performance of ML-based decision support tools. To this end, this study introduces a harmonization preprocessing protocol for image classification within a heterogeneous fluorescence dataset in terms of image acquisition parameters and presents two state-of-the-art feature-based approaches for differentiating three classes of nuclei labelled by an expert based on (a) pathomics analysis scoring an accuracy (ACC) up to 0.957 ± 0.105, and, (b) transfer learning model exhibiting ACC up-to 0.951 ± 0.05. The proposed analysis pipelines offer good differentiation performance in the examined fluorescence histology image dataset despite the heterogeneity due to the lack of a standardized image acquisition protocol. View Full-Text
Keywords: fluorescence image classification; pathomics; machine learning; transfer learning; deep learning fluorescence image classification; pathomics; machine learning; transfer learning; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Ioannidis, G.S.; Trivizakis, E.; Metzakis, I.; Papagiannakis, S.; Lagoudaki, E.; Marias, K. Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset. Appl. Sci. 2021, 11, 3796. https://doi.org/10.3390/app11093796

AMA Style

Ioannidis GS, Trivizakis E, Metzakis I, Papagiannakis S, Lagoudaki E, Marias K. Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset. Applied Sciences. 2021; 11(9):3796. https://doi.org/10.3390/app11093796

Chicago/Turabian Style

Ioannidis, Georgios S., Eleftherios Trivizakis, Ioannis Metzakis, Stilianos Papagiannakis, Eleni Lagoudaki, and Kostas Marias. 2021. "Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset" Applied Sciences 11, no. 9: 3796. https://doi.org/10.3390/app11093796

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop