%0 Conference Proceedings
%T Automatic Semi-quantitative Histological Assessment of Tissue Traits Using a Smart Web Application
%+ Department of Computer Engineering and Informatics [Patras]
%+ Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf  [Hamburg] (UKE)
%A Giannou, Olympia
%A Zazara, Dimitra, E.
%A Giannou, Anastasios, D.
%A Arck, Petra, Clara
%A Pavlidis, Georgios
%Z Part 3: Deep Learning - Convolutional
%< avec comité de lecture
%@ 978-3-031-08332-7
%( IFIP Advances in Information and Communication Technology
%B 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Greece
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y John Macintyre
%Y Paulo Cortez
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-646
%N Part I
%P 180-191
%8 2022-06-17
%D 2022
%R 10.1007/978-3-031-08333-4_15
%K Lung
%K Asthma
%K Tissue
%K Machine learning
%K Classification
%K CNN
%K Web application
%Z Computer Science [cs]Conference papers
%X A smart web application suitable for classifying goblet cell hyperplasia and level of mucus production in stained lung tissues from mice with experimentally induced allergic asthma. Multiple trainer-model approaches are investigated and proposed in this manuscript, based on machine learning techniques, which provide a technological evolution in the analysis of traits of biomedical imaging. Several schemes, which consist of pre-trained image classifiers on ImageNet, are analyzed and compared each other. Lung tissue images of mice with allergic asthma, depicting mucus-containing periodic acid-Schiff (PAS) positive bronchial cells, are fed as input datasets. The performance of each model is evaluated, based on a variety of metrics: accuracy, recall, precision, cross entropy, f1-score, confusion matrix. Such a web tool could contribute to biomedical research by providing an automated standardized way to determine phenotypic severity of histological traits based on a semi-quantitative scoring scale.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-04317177/document
%2 https://inria.hal.science/hal-04317177/file/527511_1_En_15_Chapter.pdf
%L hal-04317177
%U https://inria.hal.science/hal-04317177
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-646