An Explainable Medical Imaging Framework for Modality Classifications Trained Using Small Datasets

Francesca Trenta, Sebastiano Battiato, Daniele Ravì

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

Abstract

With the huge expansion of artificial intelligence in medical imaging, many clinical warehouses, medical centres and research communities, have organized patients’ data in well-structured datasets. These datasets are one of the key elements to train AI-enabled solutions. Additionally, the value of such datasets depends on the quality of the underlying data. To maintain the desired high-quality standard, these datasets are actively cleaned and continuously expanded. This labelling process is time-consuming and requires clinical expertise even when a simple classification task must be performed. Therefore, in this work, we propose to tackle this problem by developing a new pipeline for the modality classification of medical images. Our pipeline has the purpose to provide an initial step in organizing a large collection of data and grouping them by modality, thus reducing the involvement of costly human raters. In our experiments, we consider 4 popular deep neural networks as the core engine of the proposed system. The results show that when limited datasets are available simpler pre-trained networks achieved better results than more complex and sophisticated architectures. We demonstrate this by comparing the considered networks on the ADNI dataset and by exploiting explainable AI techniques that help us to understand our hypothesis. Still today, many medical imaging studies make use of limited datasets, therefore we believe that our contribution is particularly relevant to drive future developments of new medical imaging technologies when limited data are available.

Original languageEnglish
Title of host publicationImage Analysis and Processing – ICIAP 2022
Subtitle of host publication21st International Conference, Proceedings, Par I
EditorsStan Sclaroff, Cosimo Distante, Marco Leo, Giovanni M. Farinella, Federico Tombari
PublisherSpringer Nature
Pages358-367
Number of pages10
Volume13231
ISBN (Electronic)978-3-031-06427-2
ISBN (Print)978-3-031-06426-5
DOIs
Publication statusPublished - 15 May 2022
Event21st International Conference on Image Analysis and Processing, ICIAP 2022 - Lecce, Italy
Duration: 23 May 202227 May 2022
Conference number: 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume13231 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Image Analysis and Processing, ICIAP 2022
Abbreviated titleICIAP 2022
Country/TerritoryItaly
CityLecce
Period23/05/2227/05/22

Keywords

  • Explainable artificial intelligence
  • Modality classification
  • Quality control

Fingerprint

Dive into the research topics of 'An Explainable Medical Imaging Framework for Modality Classifications Trained Using Small Datasets'. Together they form a unique fingerprint.

Cite this