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Bioengineering
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Machine Learning Techniques to Diagnose Breast Cancer
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Machine Learning Techniques to Diagnose Breast Cancer

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 7930

Special Issue Editors

Prof. Dr. Junchang Xin

E-Mail Website
Guest Editor
College of Computer Science and Engineering, Northeastern University, Shenyang, China
Interests: blockchain technology; medical informatics; big data management and analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongyang Wang

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Interests: computer-aided diagnosis; medical informatics; medical image processing; human brain connectome

Special Issue Information

Dear Colleagues,

Machine learning, especially artificial intelligence, has significant applications in the detection and diagnosis of breast cancer. Radiomics and deep learning utilize a combination of huge amounts of multi-modal data and better calculation methods to motivate the development of analytical techniques of medical figures of breast cancer. Machine learning will affect all perspectives of the course of medication of breast cancer patients based on images, including detection, diagnosis, prognosis, evaluation of therapeutic reaction, risk analysis, etc. In current researches, the main method used is the open-source artificial intelligence method. The open-source structure and the training weights provide convenience for the artificial intelligence methods to be used upon data of breast cancer. However, many elements prevent it from clinical application experiment, including a lack of explanation, standardization, reproducibility, and generalizability, among other things. As a result, the Special Issue ’Machine Learning Techniques to Diagnose Breast Cancer‘ aims to illustrate the development of machine learning methods in clinical breast cancer and the new applications involved in all processes of diagnosis and therapy of breast disease patients from a combination of radiomics and machine learning methods. It also seeks to provide a reference for clinical application for the researchers.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Machine learning for breast cancer diagnostic;
  • Machine learning for breast cancer detection;
  • Automatic breast cancer image pre-processing;
  • Breast cancer image segmentation;
  • Prognostic prediction methods for breast cancer;
  • Breast cancer risk assessment.
  • Interpretable and explanation CAD models for breast cancer;
  • Other machine models for any cancer.

Prof. Dr. Junchang Xin
Dr. Zhongyang Wang
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • artificial intelligence
  • machine learning
  • CAD
  • interpretation
  • clinical application

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Published Papers (3 papers)

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Research

17 pages, 921 KiB  
Article
Transformative Approaches in Breast Cancer Detection: Integrating Transformers into Computer-Aided Diagnosis for Histopathological Classification
by Majed Alwateer, Amna Bamaqa, Mohamed Farsi, Mansourah Aljohani, Mohamed Shehata and Mostafa A. Elhosseini
Bioengineering 2025, 12(3), 212; https://doi.org/10.3390/bioengineering12030212 - 20 Feb 2025
Viewed by 710
Abstract
Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide, necessitating advancements in diagnostic methodologies to improve early detection and treatment outcomes. This study proposes a novel twin-stream approach for histopathological image classification, utilizing both histopathologically inherited and vision-based features [...] Read more.
Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide, necessitating advancements in diagnostic methodologies to improve early detection and treatment outcomes. This study proposes a novel twin-stream approach for histopathological image classification, utilizing both histopathologically inherited and vision-based features to enhance diagnostic precision. The first stream utilizes Virchow2, a deep learning model designed to extract high-level histopathological features, while the second stream employs Nomic, a vision-based transformer model, to capture spatial and contextual information. The fusion of these streams ensures a comprehensive feature representation, enabling the model to achieve state-of-the-art performance on the BACH dataset. Experimental results demonstrate the superiority of the twin-stream approach, with a mean accuracy of 98.60% and specificity of 99.07%, significantly outperforming single-stream methods and related studies. Statistical analyses, including paired t-tests, ANOVA, and correlation studies, confirm the robustness and reliability of the model. The proposed approach not only improves diagnostic accuracy but also offers a scalable and efficient solution for clinical applications, addressing the challenges of resource constraints and increasing diagnostic demands. Full article
(This article belongs to the Special Issue Machine Learning Techniques to Diagnose Breast Cancer)
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17 pages, 17628 KiB  
Article
AI-Based Cancer Detection Model for Contrast-Enhanced Mammography
by Clément Jailin, Sara Mohamed, Razvan Iordache, Pablo Milioni De Carvalho, Salwa Yehia Ahmed, Engy Abdullah Abdel Sattar, Amr Farouk Ibrahim Moustafa, Mohammed Mohammed Gomaa, Rashaa Mohammed Kamal and Laurence Vancamberg
Bioengineering 2023, 10(8), 974; https://doi.org/10.3390/bioengineering10080974 - 17 Aug 2023
Cited by 12 | Viewed by 2865
Abstract
Background: The recent development of deep neural network models for the analysis of breast images has been a breakthrough in computer-aided diagnostics (CAD). Contrast-enhanced mammography (CEM) is a recent mammography modality providing anatomical and functional imaging of the breast. Despite the clinical benefits [...] Read more.
Background: The recent development of deep neural network models for the analysis of breast images has been a breakthrough in computer-aided diagnostics (CAD). Contrast-enhanced mammography (CEM) is a recent mammography modality providing anatomical and functional imaging of the breast. Despite the clinical benefits it could bring, only a few research studies have been conducted around deep-learning (DL) based CAD for CEM, especially because the access to large databases is still limited. This study presents the development and evaluation of a CEM-CAD for enhancing lesion detection and breast classification. Materials & Methods: A deep learning enhanced cancer detection model based on a YOLO architecture has been optimized and trained on a large CEM dataset of 1673 patients (7443 images) with biopsy-proven lesions from various hospitals and acquisition systems. The evaluation was conducted using metrics derived from the free receiver operating characteristic (FROC) for the lesion detection and the receiver operating characteristic (ROC) to evaluate the overall breast classification performance. The performances were evaluated for different types of image input and for each patient background parenchymal enhancement (BPE) level. Results: The optimized model achieved an area under the curve (AUROC) of 0.964 for breast classification. Using both low-energy and recombined image as inputs for the DL model shows greater performance than using only the recombined image. For the lesion detection, the model was able to detect 90% of all cancers with a false positive (non-cancer) rate of 0.128 per image. This study demonstrates a high impact of BPE on classification and detection performance. Conclusion: The developed CEM CAD outperforms previously published papers and its performance is comparable to radiologist-reported classification and detection capability. Full article
(This article belongs to the Special Issue Machine Learning Techniques to Diagnose Breast Cancer)
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14 pages, 1286 KiB  
Article
Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification
by Gonzalo Iñaki Quintana, Zhijin Li, Laurence Vancamberg, Mathilde Mougeot, Agnès Desolneux and Serge Muller
Bioengineering 2023, 10(5), 534; https://doi.org/10.3390/bioengineering10050534 - 27 Apr 2023
Cited by 10 | Viewed by 3821
Abstract
Recent progress in deep learning (DL) has revived the interest on DL-based computer aided detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches are one of the main state-of-the-art techniques for 2D mammogram image classification, but they are intrinsically limited by [...] Read more.
Recent progress in deep learning (DL) has revived the interest on DL-based computer aided detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches are one of the main state-of-the-art techniques for 2D mammogram image classification, but they are intrinsically limited by the choice of patch size, as there is no unique patch size that is adapted to all lesion sizes. In addition, the impact of input image resolution on performance is not yet fully understood. In this work, we study the impact of patch size and image resolution on the classifier performance for 2D mammograms. To leverage the advantages of different patch sizes and resolutions, a multi patch-size classifier and a multi-resolution classifier are proposed. These new architectures perform multi-scale classification by combining different patch sizes and input image resolutions. The AUC is increased by 3% on the public CBIS-DDSM dataset and by 5% on an internal dataset. Compared with a baseline single patch size and single resolution classifier, our multi-scale classifier reaches an AUC of 0.809 and 0.722 in each dataset. Full article
(This article belongs to the Special Issue Machine Learning Techniques to Diagnose Breast Cancer)
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