Explainable Artificial Intelligence in Medical Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1377

Special Issue Editor

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Guest Editor
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: disease recognition using artificial intelligence methods; digital health; multimodal interfaces; biomedical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of artificial intelligence (AI) and machine learning (ML) technologies has revolutionized various sectors, with healthcare being one of the most significantly impacted. In particular, AI and ML have shown immense potential in the field of medical imaging, aiding in the detection, diagnosis, and prognosis of various diseases. However, the "black box" nature of these technologies often hinders their full acceptance in clinical practice. The need to understand, interpret, and explain AI-driven decisions is paramount, especially in the high-stakes field of healthcare.

This Special Issue of Diagnostics, titled "Explainable Artificial Intelligence in Medical Imaging", aims to gather and showcase the latest research, developments, and ideas that focus on making AI and ML models in medical imaging more interpretable and explainable. We invite contributions that address the challenges of integrating explainable AI (XAI) into medical imaging, including (but not limited to), the development of novel XAI models, the validation of XAI in clinical settings, ethical considerations, and the impact of XAI on patient outcomes.

Call for Papers

We invite researchers and practitioners from academia, industry, and healthcare to submit original research articles, review articles, and brief reports that enhance the field of explainable AI in medical imaging. Topics of interest include, but are not limited to, the following:

  1. Novel XAI models for medical imaging;
  2. The clinical validation of XAI models;
  3. The impact of XAI on diagnostic accuracy and patient outcomes;
  4. Ethical considerations in the application of XAI in medical imaging;
  5. The successful implementation of XAI in medical imaging;
  6. Strategies for integrating XAI into existing diagnostics systems;
  7. Patient and healthcare professional perspectives on XAI in medical imaging.

Dr. Robertas Damaševičius
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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16 pages, 6376 KiB  
Building Automation Pipeline for Diagnostic Classification of Sporadic Odontogenic Keratocysts and Non-Keratocysts Using Whole-Slide Images
by Samahit Mohanty, Divya B. Shivanna, Roopa S. Rao, Madhusudan Astekar, Chetana Chandrashekar, Raghu Radhakrishnan, Shylaja Sanjeevareddygari, Vijayalakshmi Kotrashetti and Prashant Kumar
Diagnostics 2023, 13(21), 3384; https://doi.org/10.3390/diagnostics13213384 - 04 Nov 2023
Viewed by 897
The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of particular interest is the odontogenic keratocyst (OKC), a developmental cyst with unique histopathological and clinical characteristics. Nevertheless, what distinguishes this cyst [...] Read more.
The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of particular interest is the odontogenic keratocyst (OKC), a developmental cyst with unique histopathological and clinical characteristics. Nevertheless, what distinguishes this cyst is its aggressive nature and high tendency for recurrence. Clinicians encounter challenges in dealing with this frequently encountered jaw lesion, as there is no consensus on surgical treatment. Therefore, the accurate and early diagnosis of such cysts will benefit clinicians in terms of treatment management and spare subjects from the mental agony of suffering from aggressive OKCs, which impact their quality of life. The objective of this research is to develop an automated OKC diagnostic system that can function as a decision support tool for pathologists, whether they are working locally or remotely. This system will provide them with additional data and insights to enhance their decision-making abilities. This research aims to provide an automation pipeline to classify whole-slide images of OKCs and non-keratocysts (non-KCs: dentigerous and radicular cysts). OKC diagnosis and prognosis using the histopathological analysis of tissues using whole-slide images (WSIs) with a deep-learning approach is an emerging research area. WSIs have the unique advantage of magnifying tissues with high resolution without losing information. The contribution of this research is a novel, deep-learning-based, and efficient algorithm that reduces the trainable parameters and, in turn, the memory footprint. This is achieved using principal component analysis (PCA) and the ReliefF feature selection algorithm (ReliefF) in a convolutional neural network (CNN) named P-C-ReliefF. The proposed model reduces the trainable parameters compared to standard CNN, achieving 97% classification accuracy. Full article
(This article belongs to the Special Issue Explainable Artificial Intelligence in Medical Imaging)
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