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Diagnostics
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Artificial Intelligence in Computational Pathology
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Artificial Intelligence in Computational Pathology

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 August 2024) | Viewed by 7525

Special Issue Editor

Dr. Mohammed M. Abdelsamea

E-Mail Website
Guest Editor
School of Computing and Digital Technology, Birmingham City University, Birmingham, UK
Interests: image processing; deep learning; machine learning; medical image analysis

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is the backbone of computational pathology workflow, which is one of the most important technologies used in the field of histopathology. In recent years, deep learning techniques have been used to learn the local hierarchical levels of representation from histopathological data in various ways (including via semi-supervised and self-supervised methods). These methods comprise a variety of promising ways to extract relevant diagnostic and prognostic features. Due to the requirements for contextual information, context-aware learning methods, such as transformer-based, graph-based, and large language models, have been adopted to provide accurate diagnoses with which to more effectively manage patients and customise treatment plans. Such models can help researchers to discover new digital biomarkers and may constitute a crucial step toward closing the translation gap between AI and clinical practice. Nevertheless, interpretable methods are still needed to understand the underlying mechanism behind the AI’s decisions. This will not only increase users’ trust in these technologies but also allow the wide use of algorithms trained for specific tasks in a way akin to what occurs human approaches. The purpose of this Special Issue is to discuss recent advances in the use of state-of-the-art AI systems for computational pathology, the challenges and opportunities inherent in the use of these methods, and their reliability in the field.

Dr. Mohammed M. Abdelsamea
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.

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Published Papers (3 papers)

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Research

20 pages, 3080 KiB  
Article
A BERT-GNN Approach for Metastatic Breast Cancer Prediction Using Histopathology Reports
by Abdullah Basaad , Shadi Basurra , Edlira Vakaj , Ahmed Karam Eldaly  and Mohammed M. Abdelsamea 
Diagnostics 2024, 14(13), 1365; https://doi.org/10.3390/diagnostics14131365 - 27 Jun 2024
Viewed by 1964
Abstract
Metastatic breast cancer (MBC) continues to be a leading cause of cancer-related deaths among women. This work introduces an innovative non-invasive breast cancer classification model designed to improve the identification of cancer metastases. While this study marks the initial exploration into predicting MBC, [...] Read more.
Metastatic breast cancer (MBC) continues to be a leading cause of cancer-related deaths among women. This work introduces an innovative non-invasive breast cancer classification model designed to improve the identification of cancer metastases. While this study marks the initial exploration into predicting MBC, additional investigations are essential to validate the occurrence of MBC. Our approach combines the strengths of large language models (LLMs), specifically the bidirectional encoder representations from transformers (BERT) model, with the powerful capabilities of graph neural networks (GNNs) to predict MBC patients based on their histopathology reports. This paper introduces a BERT-GNN approach for metastatic breast cancer prediction (BG-MBC) that integrates graph information derived from the BERT model. In this model, nodes are constructed from patient medical records, while BERT embeddings are employed to vectorise representations of the words in histopathology reports, thereby capturing semantic information crucial for classification by employing three distinct approaches (namely univariate selection, extra trees classifier for feature importance, and Shapley values to identify the features that have the most significant impact). Identifying the most crucial 30 features out of 676 generated as embeddings during model training, our model further enhances its predictive capabilities. The BG-MBC model achieves outstanding accuracy, with a detection rate of 0.98 and an area under curve (AUC) of 0.98, in identifying MBC patients. This remarkable performance is credited to the model’s utilisation of attention scores generated by the LLM from histopathology reports, effectively capturing pertinent features for classification. Full article
(This article belongs to the Special Issue Artificial Intelligence in Computational Pathology)
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25 pages, 6398 KiB  
Article
Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification
by Md. Abul Ala Walid, Swarnali Mollick, Pintu Chandra Shill, Mrinal Kanti Baowaly, Md. Rabiul Islam, Md. Martuza Ahamad, Manal A. Othman and Md Abdus Samad
Diagnostics 2023, 13(19), 3155; https://doi.org/10.3390/diagnostics13193155 - 9 Oct 2023
Cited by 4 | Viewed by 2492
Abstract
The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution [...] Read more.
The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals. Full article
(This article belongs to the Special Issue Artificial Intelligence in Computational Pathology)
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33 pages, 6761 KiB  
Article
Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas
by Mohammed Hamdi, Ebrahim Mohammed Senan, Mukti E. Jadhav, Fekry Olayah, Bakri Awaji and Khaled M. Alalayah
Diagnostics 2023, 13(13), 2258; https://doi.org/10.3390/diagnostics13132258 - 4 Jul 2023
Cited by 6 | Viewed by 2257
Abstract
Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Biopsies taken from the [...] Read more.
Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Biopsies taken from the patient are the gold standard for lymphoma analysis. Glass slides under a microscope are converted into whole slide images (WSI) to be analyzed by AI techniques through biomedical image processing. Because of the multiplicity of types of malignant lymphomas, manual diagnosis by pathologists is difficult, tedious, and subject to disagreement among physicians. The importance of artificial intelligence (AI) in the early diagnosis of malignant lymphoma is significant and has revolutionized the field of oncology. The use of AI in the early diagnosis of malignant lymphoma offers numerous benefits, including improved accuracy, faster diagnosis, and risk stratification. This study developed several strategies based on hybrid systems to analyze histopathological images of malignant lymphomas. For all proposed models, the images and extraction of malignant lymphocytes were optimized by the gradient vector flow (GVF) algorithm. The first strategy for diagnosing malignant lymphoma images relied on a hybrid system between three types of deep learning (DL) networks, XGBoost algorithms, and decision tree (DT) algorithms based on the GVF algorithm. The second strategy for diagnosing malignant lymphoma images was based on fusing the features of the MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models and classifying them by XGBoost and DT algorithms based on the ant colony optimization (ACO) algorithm. The color, shape, and texture features, which are called handcrafted features, were extracted by four traditional feature extraction algorithms. Because of the similarity in the biological characteristics of early-stage malignant lymphomas, the features of the fused MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models were combined with the handcrafted features and classified by the XGBoost and DT algorithms based on the ACO algorithm. We concluded that the performance of the two networks XGBoost and DT, with fused features between DL networks and handcrafted, achieved the best performance. The XGBoost network based on the fused features of MobileNet-VGG16 and handcrafted features resulted in an AUC of 99.43%, accuracy of 99.8%, precision of 99.77%, sensitivity of 99.7%, and specificity of 99.8%. This highlights the significant role of AI in the early diagnosis of malignant lymphoma, offering improved accuracy, expedited diagnosis, and enhanced risk stratification. This study highlights leveraging AI techniques and biomedical image processing; the analysis of whole slide images (WSI) converted from biopsies allows for improved accuracy, faster diagnosis, and risk stratification. The developed strategies based on hybrid systems, combining deep learning networks, XGBoost and decision tree algorithms, demonstrated promising results in diagnosing malignant lymphoma images. Furthermore, the fusion of handcrafted features with features extracted from DL networks enhanced the performance of the classification models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Computational Pathology)
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