Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features.

Nicola Altini, Tommaso Maria Marvulli, Mariapia Caputo, Eliseo Mattioli, Berardino Prencipe, Giacomo Donato Cascarano, Antonio Brunetti, Stefania Tommasi, Vitoantonio Bevilacqua, Simona De Summa, Francesco Alfredo Zito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Multi-class tissue classification from histological images is a complex challenge. The gold standard still relies on manual assessment by a trained pathologist, but it is a time-expensive task with issues about intra- and inter-operator variability. The rise of computational models in Digital Pathology has the potential to revolutionize the field. Historically, image classifiers relied on handcrafted feature extraction, combined with statistical classifiers, as Support Vector Machines (SVMs) or Artificial Neural Networks (ANNs). In recent years, there has been a tremendous growth in Deep Learning (DL), for all the image recognition tasks, including, of course, those concerning medical images. Thanks to DL, it is now possible to also learn the process of capturing the most relevant features from the image, easing the design of specialized classification algorithms and improving the performance. An important problem of DL is that it requires tons of training data, which is not easy to obtain in medical domain, since images have to be annotated by expert physicians. In this work, we extensively compared three classes of approaches for the multi-class tissue classification task: (1) extraction of handcrafted features with the adoption of a statistical classifier; (2) extraction of deep features using the transfer learning paradigm, then exploiting SVM or ANN classifiers; (3) fine-tuning of deep classifiers. After a cross-validation on a publicly available dataset, we validated our results on two independent test sets, obtaining an accuracy of 97% and of 77%, respectively. The second test set has been provided by the Pathology Department of IRCCS Istituto Tumori Giovanni Paolo II and has been made publicly available (http://doi.org/10.5281/zenodo.4785131 ).

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings
EditorsDe-Shuang Huang, Kang-Hyun Jo, Jianqiang Li, Valeriya Gribova, Prashan Premaratne
PublisherSpringer Science and Business Media Deutschland GmbH
Pages512-525
Number of pages14
ISBN (Print)9783030845216
DOIs
Publication statusPublished - 2021
Event17th International Conference on Intelligent Computing, ICIC 2021 - Shenzhen, China
Duration: Aug 12 2021Aug 15 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12836 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Intelligent Computing, ICIC 2021
Country/TerritoryChina
CityShenzhen
Period8/12/218/15/21

Keywords

  • Colorectal cancer
  • Deep learning
  • Handcrafted features
  • Histological tissue classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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