Advances in Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging—2nd Edition

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: 30 April 2025 | Viewed by 4729

Special Issue Editor


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Guest Editor
Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
Interests: deep learning; radiomics; histopathology; medical image analysis; image segmentation; image classification; CAD systems
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Special Issue Information

Dear Colleagues,

Cancer ranks the second most common cause of death in many countries, following cardiovascular diseases [1]. Therefore, early detection and diagnosis are crucial for improving the 5-year survival rate [2]. Screening examination plays an essential role in diagnosing diseases [3], requiring physicians to interpret many medical images. However, human interpretation has many limitations, including inaccuracy, distraction, and fatigue, which may lead to false positives and false negatives that lead to improper treatment. Therefore, a computer-aided diagnosis (CAD) system is needed as a second opinion system to diagnose ambiguous cases to solve these limitations. Computer-aided diagnostic (CAD) systems use classical image processing, computer vision, machine learning, and deep learning methods for image analysis. Using image classification or segmentation algorithms, they find a region of interest (ROI) pointing to a specific location within the given image or the outcome of interest in the form of a label pointing to a diagnosis or prognosis. This Special Issue focuses on advanced CAD methods that use artificial intelligence (AI) approaches in various imaging modalities, such as X-ray, computed tomography (CT), positron emission tomography (PET), ultrasound, MRI, immunohistochemistry, and hematoxylin and eosin (H&E) whole slide images (WSIs), toward the end diagnosis or prognosis.

[1] Huang, X.; Xiao, R.; Pan, S.; Yang, X.; Yuan, W.; Tu, Z.; et al. Uncovering the roles of long non-coding RNAS in cancer stem cells. J. Hematol. Oncol. 2017, 10, 62. doi: 10.1186/s13045-017-0428-9.

[2] Mohaghegh, P.; Rockall, A.G. Imaging strategy for early ovarian cancer: Characterization of adnexal masses with conventional and advanced imaging techniques. Radiographics 2012, 32, 1751–1773.

[3] Sarigoz, T.; Ertan, T.; Topuz, O.; Sevim, Y.; Cihan, Y. Role of digital infrared thermal imaging in the diagnosis of breast mass: A pilot study. Infrared Phys. Technol. 2018, 91, 214–219.

Dr. Farhan Akram
Guest Editor

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Keywords

  • cancer diagnosis
  • medical images
  • electronic health records
  • machine learning
  • deep learning
  • artificial intelligence
  • explainable AI models
  • multi-modal analysis
  • federated learning
  • CAD systems

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Published Papers (3 papers)

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Research

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20 pages, 4128 KiB  
Article
Equilibrium Optimization-Based Ensemble CNN Framework for Breast Cancer Multiclass Classification Using Histopathological Image
by Yasemin Çetin-Kaya
Diagnostics 2024, 14(19), 2253; https://doi.org/10.3390/diagnostics14192253 - 9 Oct 2024
Viewed by 695
Abstract
Background: Breast cancer is one of the most lethal cancers among women. Early detection and proper treatment reduce mortality rates. Histopathological images provide detailed information for diagnosing and staging breast cancer disease. Methods: The BreakHis dataset, which includes histopathological images, is [...] Read more.
Background: Breast cancer is one of the most lethal cancers among women. Early detection and proper treatment reduce mortality rates. Histopathological images provide detailed information for diagnosing and staging breast cancer disease. Methods: The BreakHis dataset, which includes histopathological images, is used in this study. Medical images are prone to problems such as different textural backgrounds and overlapping cell structures, unbalanced class distribution, and insufficiently labeled data. In addition to these, the limitations of deep learning models in overfitting and insufficient feature extraction make it extremely difficult to obtain a high-performance model in this dataset. In this study, 20 state-of-the-art models are trained to diagnose eight types of breast cancer using the fine-tuning method. In addition, a comprehensive experimental study was conducted to determine the most successful new model, with 20 different custom models reported. As a result, we propose a novel model called MultiHisNet. Results: The most effective new model, which included a pointwise convolution layer, residual link, channel, and spatial attention module, achieved 94.69% accuracy in multi-class breast cancer classification. An ensemble model was created with the best-performing transfer learning and custom models obtained in the study, and model weights were determined with an Equilibrium Optimizer. The proposed ensemble model achieved 96.71% accuracy in eight-class breast cancer detection. Conclusions: The results show that the proposed model will support pathologists in successfully diagnosing breast cancer. Full article
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Review

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19 pages, 3177 KiB  
Review
Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques
by Zaka Ur Rehman, Mohammad Faizal Ahmad Fauzi, Wan Siti Halimatul Munirah Wan Ahmad, Fazly Salleh Abas, Phaik Leng Cheah, Seow Fan Chiew and Lai-Meng Looi
Diagnostics 2024, 14(18), 2089; https://doi.org/10.3390/diagnostics14182089 - 21 Sep 2024
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Abstract
Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20–25% of breast cancers, can be assessed [...] Read more.
Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20–25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques. Full article
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16 pages, 254 KiB  
Review
Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review
by Petar Uchikov, Usman Khalid, Krasimir Kraev, Bozhidar Hristov, Maria Kraeva, Tihomir Tenchev, Dzhevdet Chakarov, Milena Sandeva, Snezhanka Dragusheva, Daniela Taneva and Atanas Batashki
Diagnostics 2024, 14(5), 528; https://doi.org/10.3390/diagnostics14050528 - 1 Mar 2024
Cited by 1 | Viewed by 2595
Abstract
Background: The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. Methods: A targeted, non-systematic review of the [...] Read more.
Background: The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. Methods: A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. Results: The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. Conclusions: Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC’s morbidity and mortality. Full article
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