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@MarcoParola
MarcoParola committed Nov 22, 2024
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
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<meta name="description" content="DESCRIPTION META TAG">
<meta property="og:title" content="SOCIAL MEDIA TITLE TAG"/>
<meta property="og:description" content="SOCIAL MEDIA DESCRIPTION TAG TAG"/>
<meta property="og:url" content="URL OF THE WEBSITE"/>
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<title>Academic Project Page</title>
<link rel="icon" type="image/x-icon" href="static/images/favicon.ico">
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">

<link rel="stylesheet" href="static/css/bulma.min.css">
<link rel="stylesheet" href="static/css/bulma-carousel.min.css">
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<body>


<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://marcoparola.github.io/" target="_blank">Marco Parola</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://galatolo.me/" target="_blank">Federico A. Galatolo</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Gaetano La Mantia</a><sup>1</sup>,</span> <br>
<span class="author-block">
<a href="http://docenti.ing.unipi.it/m.cimino/" target="_blank">Mario GCA Cimino</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Giuseppina Campisi</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Olga Di Fede</a><sup>2,3</sup>,</span>
</div>

<div class="is-size-7 publication-authors">
<span class="author-block"><sup>1</sup>Department of Information Engineering, University of Pisa, Italy</span><br>
<span class="author-block"><sup>2</sup>Department Di.Chir.On.S, University of Palermo, Palermo, Italy</span>
<span class="author-block"><sup>3</sup>Unit of Oral Medicine and Dentistry for fragile patients, Department of Rehabilitation, fragility, and continuity of care University Hospital Palermo, Palermo, Italy</span>
<span class="author-block"><sup>4</sup>Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy</span>
</div>

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class="external-link button is-normal is-rounded is-dark">
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<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
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<!-- Github link -->
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<a href="https://github.com/MarcoParola/oral2" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>

<!-- ArXiv abstract Link -->
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class="external-link button is-normal is-rounded is-dark">
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<span>Data</span>
</a>
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</div>
</div>
</div>
</div>
</div>
</section>



<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and costly data acquisition. A cost-efficient, computerized screening system is crucial for early disease detection, minimizing the need for expert intervention and expensive analysis. Besides, transparency is essential to align these systems with critical sector applications. Explainable Artificial Intelligence (XAI) provides techniques for understanding models. However, current XAI is mostly data-driven and focused on addressing developers’ requirements of improving models rather than clinical users’ demands for expressing relevant insights. Among different XAI strategies, we propose a solution composed of Case-Based Reasoning paradigm to provide visual output explanations and Informed Deep Learning (IDL) to integrate medical knowledge within the system. A key aspect of our solution lies in its capability to handle data imperfections, including labeling inaccuracies and artifacts, thanks to an ensemble architecture on top of the deep learning (DL) workflow. We conducted several experimental benchmarks on a dataset collected in collaboration with medical centers. Our findings reveal that employing the IDL approach yields an accuracy of 85%, surpassing the 77% accuracy achieved by DL alone. Furthermore, we measured the human-centered explainability of the two approaches and IDL generates explanations more congruent with the clinical user demands.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->



<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
@article{PAROLA2024102433,
title = {Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning},
journal = {Computerized Medical Imaging and Graphics},
volume = {117},
pages = {102433},
year = {2024},
issn = {0895-6111},
doi = {https://doi.org/10.1016/j.compmedimag.2024.102433},
url = {https://www.sciencedirect.com/science/article/pii/S0895611124001101},
author = {Marco Parola and Federico A. Galatolo and Gaetano {La Mantia} and Mario G.C.A. Cimino and Giuseppina Campisi and Olga {Di Fede}},
keywords = {Oral cancer, Oncology, Medical imaging, Case-based reasoning, Informed deep learning, Explainable artificial intelligence},
}
</code></pre>
</div>
</section>
<!--End BibTex citation -->


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