UTILIZING AUTOENCODERS FOR ANALYSIS AND CLASSIFICATION OF MICROSCOPIC IN SITU HYBRIDIZATION IMAGES

被引：0
|
作者：
Yanev, Aleksandar A. [1 ]
Momcheva, Galina D. [2 ]
Pavlov, Stoyan P. [3 ,4 ]
机构：
[1] High Sch Math Dr Petar Beron, Aleksander Stamboliyski Blvd, Varna 9000, Bulgaria
[2] Bulgarian Acad Sci, Inst Math & Informat, Akad Georgi Bonchev St,Bl 8, Sofia 1113, Bulgaria
[3] Med Univ Varna, Dept Anat & Cell Biol, 55 Prof Marin Drinov St, Varna 9002, Bulgaria
[4] Med Univ Prof Dr Paraskev Stoyanov, Adv Computat Bioimaging Res Inst, Varna, Bulgaria
来源：
COMPTES RENDUS DE L ACADEMIE BULGARE DES SCIENCES | 2023年 / 76卷 / 11期
关键词：
artificial neural networks; deep learning autoencoders; image analysis; unsupervised learning; fuzzy clustering;
D O I：
10.7546/CRABS.2023.11.11
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Currently, analysis of microscopic In Situ Hybridization (ISH) images is done manually by experts. Precise evaluation and classification of such micro-scopic images can ease experts' work and reveal further insights about the data. In this work, we propose a deep-learning workflow to detect and classify areas of microscopic images with similar levels of gene expression. Analysis of the data is done by employing a type of ANN - Deep Learning Autoencoders - suitable for unsupervised learning. The model's performance is optimised by balancing the latent layers' length and complexity and fine-tuning hyperparameters. The results are validated by adapting the mean-squared error (MSE) metric and comparison to expert's evaluation. Reconstruction of the whole-scale micro-scopic images is used to summarise and visualise the results.
引用
收藏
页码：1733 / 1742
页数：10
相关论文
共 50 条
  • [1] DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders
    Cohen, Ido
    David, Eli
    Netanyahu, Nathan S.
    Liscovitch, Noa
    Chechik, Gal
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, PT II, 2017, 10614 : 287 - 296
  • [2] Automatic signal classification in fluorescence in situ hybridization images
    Lerner, B
    Clocksin, WF
    Dhanjal, S
    Hultén, MA
    Bishop, CM
    CYTOMETRY, 2001, 43 (02): : 87 - 93
  • [3] Classification of fluorescence in situ hybridization images using belief networks
    Malka, R
    Lerner, B
    PATTERN RECOGNITION LETTERS, 2004, 25 (16) : 1777 - 1785
  • [4] Gabor Features for the Classification and Evaluation of Chromogenic In-Situ Hybridization Images
    Pavlov, Stoyan
    Momcheva, Galina
    Burlakova, Pavlina
    Atanasov, Simeon
    Stoyanov, Dimo
    Ivanov, Martin
    Tonchev, Anton
    CONTEMPORARY METHODS IN BIOINFORMATICS AND BIOMEDICINE AND THEIR APPLICATIONS, 2022, 374 : 375 - 383
  • [5] Automated detection and analysis of fluorescent in situ hybridization spots depicted in digital microscopic images of Pap-smear specimens
    Wang, Xingwei
    Zheng, Bin
    Li, Shibo
    Zhang, Roy
    Mulvihill, John J.
    Chen, Wei R.
    Liu, Hong
    JOURNAL OF BIOMEDICAL OPTICS, 2009, 14 (02)
  • [6] Classification of Multicolor Fluorescence In Situ Hybridization (M-FISH) Images With Sparse Representation
    Cao, Hongbao
    Deng, Hong-Wen
    Li, Marilyn
    Wang, Yu-Ping
    IEEE TRANSACTIONS ON NANOBIOSCIENCE, 2012, 11 (02) : 111 - 118
  • [7] Feasibility of Haralick's Texture Features for the Classification of Chromogenic In-situ Hybridization Images
    Pavlov, Stoyan
    Momcheva, Galina
    Burlakova, Pavlina
    Atanasov, Simeon
    Stoyanov, Dimo
    Ivanov, Martin
    Tonchev, Anton
    PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON BIOMEDICAL INNOVATIONS AND APPLICATIONS (BIA 2020), 2020, : 65 - 68
  • [8] Comparative Analysis of Gene Expression Analysis Methods for RNA in Situ Hybridization Images
    Ariotta, Valeria
    Azzalini, Eros
    Canzonieri, Vincenzo
    Hautaniemi, Sampsa
    Bonin, Serena
    JOURNAL OF MOLECULAR DIAGNOSTICS, 2024, 26 (10): : 931 - 942
  • [9] CLASSIFICATION OF ELECTRON-MICROSCOPIC IMAGES BY MULTIVARIATE MATRIX ANALYSIS
    KAPP, OH
    XIMEN, JY
    OPTIK, 1985, 70 (04): : 146 - 151
  • [10] A Multi-level Thresholding Based Segmentation Method for Microscopic Fluorescence In Situ Hybridization (FISH) Images
    Kabakci, Kaan A.
    Capar, Abdulkerim
    Toreyin, B. Ugur
    Akkoc, Mertkan
    Borazan, Ozan
    Turkmen, Ilknur
    Ata, Lutfiye Durak
    2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU), 2016, : 849 - 852
12345