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Applied Sciences
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Artificial Intelligence in Screening Mammography: Recent Advances and Tools in...
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Artificial Intelligence in Screening Mammography: Recent Advances and Tools in Cancer Detection and Diagnosis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 11110

Special Issue Editors

Dr. Athanasios Koutras

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, University of Peloponnese, 26334 Patras, Greece
Interests: medical imaging; deep learning; breast cancer diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Ioanna Christoyianni

E-Mail Website
Guest Editor
Electrical & Computer Engineering Department, University of Patras, 26504 Patras, Greece
Interests: medical image processing; breast cancer detection; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. George Apostolopoulos

E-Mail Website
Guest Editor
Electrical & Computer Engineering Department, University of Patras, 26504 Patras, Greece
Interests: medical imaging; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Dermatas Evangelos

E-Mail Website
Guest Editor
Department of Computer Engineering & Informatics, University of Patras, 26504 Patras, Greece
Interests: medical imaging; deep learning; breast cancer diagnosis; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Breast cancer is a major health issue and still a leading cause of fatality among women worldwide. Mammography remains the foremost effective procedure for the early detection and diagnosis of breast cancer. The aim of this Special Issue is to present the recent advances in the detection and diagnosis of cancerous regions in mammograms using machine learning and deep learning algorithms. We particularly welcome submissions that will utilize different mammography modalities (separately or in combination) such as digital mammography (DM), tomosynthesis, ultrasound or MRI in developing systems to assist the diagnosis (CADx) and/or the detection (CADe) of regions of suspicion in mammograms. Submissions can also include but are not limited to novel feature extraction techniques for breast cancer detection and diagnosis, transfer learning and deep learning architectures, open access databases for breast cancer research, generative adversarial network (GAN) architectures that overcome the problem of small data sets etc.

The intent of this Special Issue is to explore where we stand and what the future holds in this important health related research topic. To that end, we invite submissions involving new techniques, methods, applications, and results, as well as review articles.

Prof. Dr. Athanasios Koutras
Dr. Ioanna Christoyianni
Dr. George Apostolopoulos
Prof. Dr. Dermatas Evangelos
Guest Editors

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. Applied Sciences 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 2400 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.

Keywords

  • digital mammography
  • machine learning
  • deep learning
  • breast cancer
  • ultrasound
  • digital breast tomosynthesis

Published Papers (3 papers)

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Research

17 pages, 14311 KiB  
Article
Computer-Aided Diagnosis System for Breast Ultrasound Reports Generation and Classification Method Based on Deep Learning
by Haojun Qin, Lei Zhang and Quan Guo
Appl. Sci. 2023, 13(11), 6577; https://0-doi-org.brum.beds.ac.uk/10.3390/app13116577 - 29 May 2023
Viewed by 1475
Abstract
Breast cancer is one of the most common malignancies that threaten women’s health. Ultrasound testing is a widespread technique employed for the early detection of tumors. However, after receiving the paper ultrasound report, most patients often have to wait for several days to [...] Read more.
Breast cancer is one of the most common malignancies that threaten women’s health. Ultrasound testing is a widespread technique employed for the early detection of tumors. However, after receiving the paper ultrasound report, most patients often have to wait for several days to receive the diagnosis results, which can increase their psychological burden and may cause treatment delay. Based on deep learning, this study designed a computer-aided diagnostic system that directly classifies benign and malignant tumors in breast ultrasound images on paper reports taken by patients, helping them obtain auxiliary diagnostic results as soon as possible. In order to segment and denoise ultrasound report images of patients, this paper proposes a breast ultrasound report generation method, which mainly includes a segmentation model, a rotating classification model and a generative model. With this method, multiple high-quality individual breast ultrasound images can be obtained from a single ultrasound report photo, improving the performance of the breast ultrasound image classification model. In order to utilize high-quality breast ultrasound images and improve classification performance, this paper proposed a breast ultrasound report classification model that includes a feature extraction module, a channel attention module and a classification module. The accuracy of the model reached 89.31%, recall rate reached 88.65%, specificity reached 89.57%, F1 score reached 89.42% and AUC reached 94.53% when input images contained noise. The method proposed in this article is more suitable for practical application scenarios and it can quickly and accurately assist patients in obtaining the benign and malignant classification results of ultrasound reports. Full article
(This article belongs to the Special Issue Artificial Intelligence in Screening Mammography: Recent Advances and Tools in Cancer Detection and Diagnosis)
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18 pages, 3457 KiB  
Article
Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
by Marcel Prodan, Elena Paraschiv and Alexandru Stanciu
Appl. Sci. 2023, 13(7), 4272; https://0-doi-org.brum.beds.ac.uk/10.3390/app13074272 - 28 Mar 2023
Cited by 5 | Viewed by 5473
Abstract
Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for [...] Read more.
Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process. Full article
(This article belongs to the Special Issue Artificial Intelligence in Screening Mammography: Recent Advances and Tools in Cancer Detection and Diagnosis)
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29 pages, 4604 KiB  
Article
The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography
by Ivan Lazic, Ferran Agullo, Susanna Ausso, Bruno Alves, Caroline Barelle, Josep Ll. Berral, Paschalis Bizopoulos, Oana Bunduc, Ioanna Chouvarda, Didier Dominguez, Dimitrios Filos, Alberto Gutierrez-Torre, Iman Hesso, Nikša Jakovljević, Reem Kayyali, Magdalena Kogut-Czarkowska, Alexandra Kosvyra, Antonios Lalas, Maria Lavdaniti, Tatjana Loncar-Turukalo, Sara Martinez-Alabart, Nassos Michas, Shereen Nabhani-Gebara, Andreas Raptopoulos, Yiannis Roussakis, Evangelia Stalika, Chrysostomos Symvoulidis, Olga Tsave, Konstantinos Votis and Andreas Charalambousadd Show full author list remove Hide full author list
Appl. Sci. 2022, 12(17), 8755; https://0-doi-org.brum.beds.ac.uk/10.3390/app12178755 - 31 Aug 2022
Cited by 6 | Viewed by 3019
Abstract
Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging [...] Read more.
Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions. Full article
(This article belongs to the Special Issue Artificial Intelligence in Screening Mammography: Recent Advances and Tools in Cancer Detection and Diagnosis)
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